Overview

Dataset statistics

Number of variables64
Number of observations29269
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 MiB
Average record size in memory512.0 B

Variable types

Numeric17
Categorical47

Warnings

DT_GERACAO has constant value "22/07/2021" Constant
HH_GERACAO has constant value "19:18:49" Constant
ANO_ELEICAO has constant value "2020" Constant
CD_TIPO_ELEICAO has constant value "2" Constant
NM_TIPO_ELEICAO has constant value "ELEIÇÃO ORDINÁRIA" Constant
NR_TURNO has constant value "1" Constant
CD_ELEICAO has constant value "426" Constant
DS_ELEICAO has constant value "Eleições Municipais 2020" Constant
DT_ELEICAO has constant value "15/11/2020" Constant
TP_ABRANGENCIA has constant value "MUNICIPAL" Constant
CD_CARGO has constant value "13" Constant
DS_CARGO has constant value "VEREADOR" Constant
TP_AGREMIACAO has constant value "PARTIDO ISOLADO" Constant
NM_COLIGACAO has constant value "PARTIDO ISOLADO" Constant
CD_MUNICIPIO_NASCIMENTO has constant value "-3" Constant
NR_PROTOCOLO_CANDIDATURA has constant value "-1" Constant
NM_UE has a high cardinality: 348 distinct values High cardinality
NM_CANDIDATO has a high cardinality: 28464 distinct values High cardinality
NM_URNA_CANDIDATO has a high cardinality: 27036 distinct values High cardinality
NM_EMAIL has a high cardinality: 13384 distinct values High cardinality
NM_MUNICIPIO_NASCIMENTO has a high cardinality: 855 distinct values High cardinality
DT_NASCIMENTO has a high cardinality: 13468 distinct values High cardinality
DS_OCUPACAO has a high cardinality: 198 distinct values High cardinality
df_index is highly correlated with SG_UE and 2 other fieldsHigh correlation
SG_UE is highly correlated with df_index and 2 other fieldsHigh correlation
SQ_CANDIDATO is highly correlated with df_index and 2 other fieldsHigh correlation
NR_CANDIDATO is highly correlated with NR_PARTIDOHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with CD_DETALHE_SITUACAO_CAND and 1 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly correlated with CD_SITUACAO_CANDIDATURAHigh correlation
NR_PARTIDO is highly correlated with NR_CANDIDATOHigh correlation
SQ_COLIGACAO is highly correlated with df_index and 2 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly correlated with CD_SITUACAO_CANDIDATURAHigh correlation
df_index is highly correlated with SG_UE and 2 other fieldsHigh correlation
SG_UE is highly correlated with df_index and 2 other fieldsHigh correlation
SQ_CANDIDATO is highly correlated with df_index and 2 other fieldsHigh correlation
NR_CANDIDATO is highly correlated with NR_PARTIDOHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with CD_DETALHE_SITUACAO_CANDHigh correlation
CD_DETALHE_SITUACAO_CAND is highly correlated with CD_SITUACAO_CANDIDATURAHigh correlation
NR_PARTIDO is highly correlated with NR_CANDIDATOHigh correlation
SQ_COLIGACAO is highly correlated with df_index and 2 other fieldsHigh correlation
NR_IDADE_DATA_POSSE is highly correlated with NR_TITULO_ELEITORAL_CANDIDATOHigh correlation
NR_TITULO_ELEITORAL_CANDIDATO is highly correlated with NR_IDADE_DATA_POSSEHigh correlation
CD_SITUACAO_CANDIDATO_PLEITO is highly correlated with CD_SITUACAO_CANDIDATO_URNAHigh correlation
CD_SITUACAO_CANDIDATO_URNA is highly correlated with CD_SITUACAO_CANDIDATO_PLEITOHigh correlation
df_index is highly correlated with ANO_ELEICAO and 12 other fieldsHigh correlation
ANO_ELEICAO is highly correlated with df_index and 12 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly correlated with df_index and 12 other fieldsHigh correlation
NR_TURNO is highly correlated with df_index and 12 other fieldsHigh correlation
CD_ELEICAO is highly correlated with df_index and 12 other fieldsHigh correlation
SG_UE is highly correlated with CD_CARGO and 8 other fieldsHigh correlation
CD_CARGO is highly correlated with df_index and 13 other fieldsHigh correlation
SQ_CANDIDATO is highly correlated with CD_SITUACAO_CANDIDATURA and 8 other fieldsHigh correlation
NR_CANDIDATO is highly correlated with CD_SITUACAO_CANDIDATURA and 8 other fieldsHigh correlation
NR_CPF_CANDIDATO is highly correlated with CD_SITUACAO_CANDIDATURA and 7 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with df_index and 17 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly correlated with df_index and 16 other fieldsHigh correlation
NR_PARTIDO is highly correlated with NR_CANDIDATO and 6 other fieldsHigh correlation
SQ_COLIGACAO is highly correlated with SQ_CANDIDATO and 6 other fieldsHigh correlation
CD_NACIONALIDADE is highly correlated with df_index and 18 other fieldsHigh correlation
CD_MUNICIPIO_NASCIMENTO is highly correlated with df_index and 18 other fieldsHigh correlation
NR_IDADE_DATA_POSSE is highly correlated with CD_GENERO and 3 other fieldsHigh correlation
NR_TITULO_ELEITORAL_CANDIDATO is highly correlated with CD_GENERO and 4 other fieldsHigh correlation
CD_GENERO is highly correlated with df_index and 20 other fieldsHigh correlation
CD_GRAU_INSTRUCAO is highly correlated with NR_PROTOCOLO_CANDIDATURA and 2 other fieldsHigh correlation
CD_ESTADO_CIVIL is highly correlated with CD_SITUACAO_CANDIDATURA and 4 other fieldsHigh correlation
CD_COR_RACA is highly correlated with NR_PROTOCOLO_CANDIDATURA and 2 other fieldsHigh correlation
CD_OCUPACAO is highly correlated with NR_PROTOCOLO_CANDIDATURA and 2 other fieldsHigh correlation
VR_DESPESA_MAX_CAMPANHA is highly correlated with NR_PROTOCOLO_CANDIDATURA and 2 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly correlated with NR_PROTOCOLO_CANDIDATURA and 2 other fieldsHigh correlation
NR_PROTOCOLO_CANDIDATURA is highly correlated with df_index and 26 other fieldsHigh correlation
NR_PROCESSO is highly correlated with CD_SITUACAO_CANDIDATO_PLEITO and 1 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_PLEITO is highly correlated with df_index and 27 other fieldsHigh correlation
CD_SITUACAO_CANDIDATO_URNA is highly correlated with df_index and 27 other fieldsHigh correlation
CD_COR_RACA is highly correlated with DS_COR_RACAHigh correlation
DS_DETALHE_SITUACAO_CAND is highly correlated with DS_SITUACAO_CANDIDATO_PLEITO and 9 other fieldsHigh correlation
NR_CANDIDATO is highly correlated with DS_COMPOSICAO_COLIGACAO and 3 other fieldsHigh correlation
DS_COMPOSICAO_COLIGACAO is highly correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
DS_COR_RACA is highly correlated with CD_COR_RACAHigh correlation
DS_SITUACAO_CANDIDATO_PLEITO is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly correlated with CD_ESTADO_CIVILHigh correlation
CD_ESTADO_CIVIL is highly correlated with DS_ESTADO_CIVILHigh correlation
CD_SITUACAO_CANDIDATO_PLEITO is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
NR_PARTIDO is highly correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
df_index is highly correlated with SG_UE and 4 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
NM_PARTIDO is highly correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
NR_IDADE_DATA_POSSE is highly correlated with NR_CPF_CANDIDATO and 1 other fieldsHigh correlation
NR_CPF_CANDIDATO is highly correlated with NR_IDADE_DATA_POSSE and 1 other fieldsHigh correlation
DS_GRAU_INSTRUCAO is highly correlated with CD_GRAU_INSTRUCAOHigh correlation
CD_GRAU_INSTRUCAO is highly correlated with DS_GRAU_INSTRUCAOHigh correlation
CD_NACIONALIDADE is highly correlated with DS_NACIONALIDADE and 1 other fieldsHigh correlation
SG_UE is highly correlated with df_index and 4 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_URNA is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
DS_SIT_TOT_TURNO is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
NR_TITULO_ELEITORAL_CANDIDATO is highly correlated with NR_IDADE_DATA_POSSE and 4 other fieldsHigh correlation
DS_GENERO is highly correlated with CD_GENEROHigh correlation
CD_SITUACAO_CANDIDATO_URNA is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
SG_UF is highly correlated with df_index and 5 other fieldsHigh correlation
DS_NACIONALIDADE is highly correlated with CD_NACIONALIDADE and 1 other fieldsHigh correlation
CD_GENERO is highly correlated with DS_GENEROHigh correlation
DS_SITUACAO_CANDIDATURA is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
CD_DETALHE_SITUACAO_CAND is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
SG_PARTIDO is highly correlated with NR_CANDIDATO and 3 other fieldsHigh correlation
SG_UF_NASCIMENTO is highly correlated with df_index and 6 other fieldsHigh correlation
SQ_CANDIDATO is highly correlated with df_index and 5 other fieldsHigh correlation
SQ_COLIGACAO is highly correlated with df_index and 5 other fieldsHigh correlation
ST_CANDIDATO_INSERIDO_URNA is highly correlated with DS_DETALHE_SITUACAO_CAND and 9 other fieldsHigh correlation
DS_DETALHE_SITUACAO_CAND is highly correlated with NM_COLIGACAO and 20 other fieldsHigh correlation
NM_COLIGACAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
DS_COMPOSICAO_COLIGACAO is highly correlated with NM_COLIGACAO and 17 other fieldsHigh correlation
DS_COR_RACA is highly correlated with NM_COLIGACAO and 15 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_PLEITO is highly correlated with DS_DETALHE_SITUACAO_CAND and 22 other fieldsHigh correlation
NR_TURNO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
DS_ESTADO_CIVIL is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
CD_ESTADO_CIVIL is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
CD_CARGO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
CD_MUNICIPIO_NASCIMENTO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
DT_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
NM_PARTIDO is highly correlated with NM_COLIGACAO and 17 other fieldsHigh correlation
CD_SIT_TOT_TURNO is highly correlated with NM_COLIGACAO and 21 other fieldsHigh correlation
CD_SITUACAO_CANDIDATURA is highly correlated with DS_DETALHE_SITUACAO_CAND and 22 other fieldsHigh correlation
NR_PROTOCOLO_CANDIDATURA is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
ST_REELEICAO is highly correlated with NM_COLIGACAO and 15 other fieldsHigh correlation
ANO_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
NM_SOCIAL_CANDIDATO is highly correlated with NM_COLIGACAO and 15 other fieldsHigh correlation
DS_GRAU_INSTRUCAO is highly correlated with NM_COLIGACAO and 15 other fieldsHigh correlation
TP_ABRANGENCIA is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
CD_TIPO_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
CD_NACIONALIDADE is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
DS_SITUACAO_CANDIDATO_URNA is highly correlated with DS_DETALHE_SITUACAO_CAND and 22 other fieldsHigh correlation
DS_SIT_TOT_TURNO is highly correlated with NM_COLIGACAO and 21 other fieldsHigh correlation
TP_AGREMIACAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
DS_GENERO is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
DS_CARGO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
SG_UF is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
DS_NACIONALIDADE is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
DS_SITUACAO_CANDIDATURA is highly correlated with DS_DETALHE_SITUACAO_CAND and 22 other fieldsHigh correlation
CD_GENERO is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
SG_PARTIDO is highly correlated with NM_COLIGACAO and 17 other fieldsHigh correlation
HH_GERACAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
SG_UF_NASCIMENTO is highly correlated with NM_COLIGACAO and 16 other fieldsHigh correlation
DT_GERACAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
DS_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
ST_DECLARAR_BENS is highly correlated with NM_COLIGACAO and 15 other fieldsHigh correlation
NM_TIPO_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
CD_ELEICAO is highly correlated with DS_DETALHE_SITUACAO_CAND and 38 other fieldsHigh correlation
ST_CANDIDATO_INSERIDO_URNA is highly correlated with DS_DETALHE_SITUACAO_CAND and 22 other fieldsHigh correlation
df_index is uniformly distributed Uniform
NM_CANDIDATO is uniformly distributed Uniform
NM_URNA_CANDIDATO is uniformly distributed Uniform
DT_NASCIMENTO is uniformly distributed Uniform
df_index has unique values Unique
SQ_CANDIDATO has unique values Unique
NR_PROCESSO has unique values Unique

Reproduction

Analysis started2021-07-24 23:00:46.808746
Analysis finished2021-07-24 23:06:24.718873
Duration5 minutes and 37.91 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct29269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15675.10817
Minimum0
Maximum31655
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:24.801669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1558.8
Q17790
median15612
Q323521
95-th percentile30013.6
Maximum31655
Range31655
Interquartile range (IQR)15731

Descriptive statistics

Standard deviation9110.373265
Coefficient of variation (CV)0.5812000254
Kurtosis-1.191725054
Mean15675.10817
Median Absolute Deviation (MAD)7860
Skewness0.0216226779
Sum458794741
Variance82998901.03
MonotonicityStrictly increasing
2021-07-24T20:06:24.924527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
208401
 
< 0.1%
208371
 
< 0.1%
208361
 
< 0.1%
208351
 
< 0.1%
208341
 
< 0.1%
208331
 
< 0.1%
208321
 
< 0.1%
208311
 
< 0.1%
208301
 
< 0.1%
Other values (29259)29259
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
316551
< 0.1%
316541
< 0.1%
316531
< 0.1%
316521
< 0.1%
316511
< 0.1%
316501
< 0.1%
316491
< 0.1%
316481
< 0.1%
316471
< 0.1%
316461
< 0.1%

DT_GERACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
22/07/2021
29269 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters292690
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22/07/2021
2nd row22/07/2021
3rd row22/07/2021
4th row22/07/2021
5th row22/07/2021

Common Values

ValueCountFrequency (%)
22/07/202129269
100.0%

Length

2021-07-24T20:06:25.161630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:25.218817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
22/07/202129269
100.0%

Most occurring characters

ValueCountFrequency (%)
2117076
40.0%
/58538
20.0%
058538
20.0%
729269
 
10.0%
129269
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234152
80.0%
Other Punctuation58538
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2117076
50.0%
058538
25.0%
729269
 
12.5%
129269
 
12.5%
Other Punctuation
ValueCountFrequency (%)
/58538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common292690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2117076
40.0%
/58538
20.0%
058538
20.0%
729269
 
10.0%
129269
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII292690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2117076
40.0%
/58538
20.0%
058538
20.0%
729269
 
10.0%
129269
 
10.0%

HH_GERACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
19:18:49
29269 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters234152
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19:18:49
2nd row19:18:49
3rd row19:18:49
4th row19:18:49
5th row19:18:49

Common Values

ValueCountFrequency (%)
19:18:4929269
100.0%

Length

2021-07-24T20:06:25.658628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:25.728770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
19:18:4929269
100.0%

Most occurring characters

ValueCountFrequency (%)
158538
25.0%
958538
25.0%
:58538
25.0%
829269
12.5%
429269
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number175614
75.0%
Other Punctuation58538
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
158538
33.3%
958538
33.3%
829269
16.7%
429269
16.7%
Other Punctuation
ValueCountFrequency (%)
:58538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common234152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
158538
25.0%
958538
25.0%
:58538
25.0%
829269
12.5%
429269
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII234152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158538
25.0%
958538
25.0%
:58538
25.0%
829269
12.5%
429269
12.5%

ANO_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
2020
29269 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters117076
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
202029269
100.0%

Length

2021-07-24T20:06:25.918051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:25.985984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
202029269
100.0%

Most occurring characters

ValueCountFrequency (%)
258538
50.0%
058538
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number117076
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
258538
50.0%
058538
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common117076
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
258538
50.0%
058538
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII117076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
258538
50.0%
058538
50.0%

CD_TIPO_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
2
29269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
229269
100.0%

Length

2021-07-24T20:06:26.165840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:26.237634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
229269
100.0%

Most occurring characters

ValueCountFrequency (%)
229269
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
229269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common29269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
229269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
229269
100.0%

NM_TIPO_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
ELEIÇÃO ORDINÁRIA
29269 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters497573
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowELEIÇÃO ORDINÁRIA
2nd rowELEIÇÃO ORDINÁRIA
3rd rowELEIÇÃO ORDINÁRIA
4th rowELEIÇÃO ORDINÁRIA
5th rowELEIÇÃO ORDINÁRIA

Common Values

ValueCountFrequency (%)
ELEIÇÃO ORDINÁRIA29269
100.0%

Length

2021-07-24T20:06:26.419370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:26.490961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
eleição29269
50.0%
ordinária29269
50.0%

Most occurring characters

ValueCountFrequency (%)
I87807
17.6%
E58538
11.8%
O58538
11.8%
R58538
11.8%
L29269
 
5.9%
Ç29269
 
5.9%
Ã29269
 
5.9%
29269
 
5.9%
D29269
 
5.9%
N29269
 
5.9%
Other values (2)58538
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter468304
94.1%
Space Separator29269
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I87807
18.8%
E58538
12.5%
O58538
12.5%
R58538
12.5%
L29269
 
6.2%
Ç29269
 
6.2%
Ã29269
 
6.2%
D29269
 
6.2%
N29269
 
6.2%
Á29269
 
6.2%
Space Separator
ValueCountFrequency (%)
29269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin468304
94.1%
Common29269
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I87807
18.8%
E58538
12.5%
O58538
12.5%
R58538
12.5%
L29269
 
6.2%
Ç29269
 
6.2%
Ã29269
 
6.2%
D29269
 
6.2%
N29269
 
6.2%
Á29269
 
6.2%
Common
ValueCountFrequency (%)
29269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII409766
82.4%
Latin 1 Sup87807
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I87807
21.4%
E58538
14.3%
O58538
14.3%
R58538
14.3%
L29269
 
7.1%
29269
 
7.1%
D29269
 
7.1%
N29269
 
7.1%
A29269
 
7.1%
Latin 1 Sup
ValueCountFrequency (%)
Ç29269
33.3%
Ã29269
33.3%
Á29269
33.3%

NR_TURNO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
1
29269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
129269
100.0%

Length

2021-07-24T20:06:26.664064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:26.733506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
129269
100.0%

Most occurring characters

ValueCountFrequency (%)
129269
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
129269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common29269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
129269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129269
100.0%

CD_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
426
29269 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters87807
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row426
2nd row426
3rd row426
4th row426
5th row426

Common Values

ValueCountFrequency (%)
42629269
100.0%

Length

2021-07-24T20:06:26.924785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:26.994809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
42629269
100.0%

Most occurring characters

ValueCountFrequency (%)
429269
33.3%
229269
33.3%
629269
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87807
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
429269
33.3%
229269
33.3%
629269
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common87807
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
429269
33.3%
229269
33.3%
629269
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII87807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
429269
33.3%
229269
33.3%
629269
33.3%

DS_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
Eleições Municipais 2020
29269 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters702456
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEleições Municipais 2020
2nd rowEleições Municipais 2020
3rd rowEleições Municipais 2020
4th rowEleições Municipais 2020
5th rowEleições Municipais 2020

Common Values

ValueCountFrequency (%)
Eleições Municipais 202029269
100.0%

Length

2021-07-24T20:06:27.181169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:27.250717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
eleições29269
33.3%
municipais29269
33.3%
202029269
33.3%

Most occurring characters

ValueCountFrequency (%)
i117076
16.7%
e58538
 
8.3%
s58538
 
8.3%
58538
 
8.3%
258538
 
8.3%
058538
 
8.3%
E29269
 
4.2%
l29269
 
4.2%
ç29269
 
4.2%
õ29269
 
4.2%
Other values (6)175614
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter468304
66.7%
Decimal Number117076
 
16.7%
Uppercase Letter58538
 
8.3%
Space Separator58538
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i117076
25.0%
e58538
12.5%
s58538
12.5%
l29269
 
6.2%
ç29269
 
6.2%
õ29269
 
6.2%
u29269
 
6.2%
n29269
 
6.2%
c29269
 
6.2%
p29269
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
E29269
50.0%
M29269
50.0%
Decimal Number
ValueCountFrequency (%)
258538
50.0%
058538
50.0%
Space Separator
ValueCountFrequency (%)
58538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin526842
75.0%
Common175614
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i117076
22.2%
e58538
11.1%
s58538
11.1%
E29269
 
5.6%
l29269
 
5.6%
ç29269
 
5.6%
õ29269
 
5.6%
M29269
 
5.6%
u29269
 
5.6%
n29269
 
5.6%
Other values (3)87807
16.7%
Common
ValueCountFrequency (%)
58538
33.3%
258538
33.3%
058538
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII643918
91.7%
Latin 1 Sup58538
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i117076
18.2%
e58538
9.1%
s58538
9.1%
58538
9.1%
258538
9.1%
058538
9.1%
E29269
 
4.5%
l29269
 
4.5%
M29269
 
4.5%
u29269
 
4.5%
Other values (4)117076
18.2%
Latin 1 Sup
ValueCountFrequency (%)
ç29269
50.0%
õ29269
50.0%

DT_ELEICAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
15/11/2020
29269 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters292690
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15/11/2020
2nd row15/11/2020
3rd row15/11/2020
4th row15/11/2020
5th row15/11/2020

Common Values

ValueCountFrequency (%)
15/11/202029269
100.0%

Length

2021-07-24T20:06:27.427873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:27.497536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
15/11/202029269
100.0%

Most occurring characters

ValueCountFrequency (%)
187807
30.0%
/58538
20.0%
258538
20.0%
058538
20.0%
529269
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234152
80.0%
Other Punctuation58538
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
187807
37.5%
258538
25.0%
058538
25.0%
529269
 
12.5%
Other Punctuation
ValueCountFrequency (%)
/58538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common292690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
187807
30.0%
/58538
20.0%
258538
20.0%
058538
20.0%
529269
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII292690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187807
30.0%
/58538
20.0%
258538
20.0%
058538
20.0%
529269
 
10.0%

TP_ABRANGENCIA
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
MUNICIPAL
29269 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters263421
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMUNICIPAL
2nd rowMUNICIPAL
3rd rowMUNICIPAL
4th rowMUNICIPAL
5th rowMUNICIPAL

Common Values

ValueCountFrequency (%)
MUNICIPAL29269
100.0%

Length

2021-07-24T20:06:27.678808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:27.749901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
municipal29269
100.0%

Most occurring characters

ValueCountFrequency (%)
I58538
22.2%
M29269
11.1%
U29269
11.1%
N29269
11.1%
C29269
11.1%
P29269
11.1%
A29269
11.1%
L29269
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter263421
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I58538
22.2%
M29269
11.1%
U29269
11.1%
N29269
11.1%
C29269
11.1%
P29269
11.1%
A29269
11.1%
L29269
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin263421
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I58538
22.2%
M29269
11.1%
U29269
11.1%
N29269
11.1%
C29269
11.1%
P29269
11.1%
A29269
11.1%
L29269
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII263421
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I58538
22.2%
M29269
11.1%
U29269
11.1%
N29269
11.1%
C29269
11.1%
P29269
11.1%
A29269
11.1%
L29269
11.1%

SG_UF
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PE
19764 
RN
9505 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters58538
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPE
2nd rowPE
3rd rowPE
4th rowPE
5th rowPE

Common Values

ValueCountFrequency (%)
PE19764
67.5%
RN9505
32.5%

Length

2021-07-24T20:06:28.019141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:28.111253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
pe19764
67.5%
rn9505
32.5%

Most occurring characters

ValueCountFrequency (%)
P19764
33.8%
E19764
33.8%
R9505
16.2%
N9505
16.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter58538
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P19764
33.8%
E19764
33.8%
R9505
16.2%
N9505
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin58538
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P19764
33.8%
E19764
33.8%
R9505
16.2%
N9505
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII58538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P19764
33.8%
E19764
33.8%
R9505
16.2%
N9505
16.2%

SG_UE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct351
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22341.67399
Minimum16004
Maximum30031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:28.223231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum16004
5-th percentile16357
Q118031
median23833
Q325135
95-th percentile26158
Maximum30031
Range14027
Interquartile range (IQR)7104

Descriptive statistics

Standard deviation3562.350455
Coefficient of variation (CV)0.1594486813
Kurtosis-1.214232104
Mean22341.67399
Median Absolute Deviation (MAD)1480
Skewness-0.6276751114
Sum653918456
Variance12690340.77
MonotonicityNot monotonic
2021-07-24T20:06:28.378914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25313896
 
3.1%
24570757
 
2.6%
17612736
 
2.5%
24910584
 
2.0%
23574561
 
1.9%
25135553
 
1.9%
17590476
 
1.6%
26298468
 
1.6%
23817468
 
1.6%
25216373
 
1.3%
Other values (341)23397
79.9%
ValueCountFrequency (%)
1600422
 
0.1%
1601237
 
0.1%
1602032
 
0.1%
16039155
0.5%
1604731
 
0.1%
1605534
 
0.1%
1606354
 
0.2%
1607121
 
0.1%
1608048
 
0.2%
1609829
 
0.1%
ValueCountFrequency (%)
30031135
 
0.5%
2633671
 
0.2%
26310247
0.8%
26298468
1.6%
26271256
0.9%
2625579
 
0.3%
2623944
 
0.2%
2621231
 
0.1%
2619034
 
0.1%
2617434
 
0.1%

NM_UE
Categorical

HIGH CARDINALITY

Distinct348
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
RECIFE
 
896
JABOATÃO DOS GUARARAPES
 
757
NATAL
 
736
OLINDA
 
584
CABO DE SANTO AGOSTINHO
 
561
Other values (343)
25735 

Length

Max length26
Median length9
Mean length10.5974239
Min length3

Characters and Unicode

Total characters310176
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGAMELEIRA
2nd rowCABO DE SANTO AGOSTINHO
3rd rowTUPARETAMA
4th rowGRAVATÁ
5th rowSERRITA

Common Values

ValueCountFrequency (%)
RECIFE896
 
3.1%
JABOATÃO DOS GUARARAPES757
 
2.6%
NATAL736
 
2.5%
OLINDA584
 
2.0%
CABO DE SANTO AGOSTINHO561
 
1.9%
PAULISTA553
 
1.9%
MOSSORÓ476
 
1.6%
CARUARU468
 
1.6%
CAMARAGIBE468
 
1.6%
PETROLINA373
 
1.3%
Other values (338)23397
79.9%

Length

2021-07-24T20:06:28.746628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
são2046
 
4.2%
do1961
 
4.0%
de1688
 
3.4%
da1097
 
2.2%
dos949
 
1.9%
recife896
 
1.8%
santo895
 
1.8%
guararapes757
 
1.5%
jaboatão757
 
1.5%
natal736
 
1.5%
Other values (390)37457
76.1%

Most occurring characters

ValueCountFrequency (%)
A51038
16.5%
O28634
 
9.2%
R25015
 
8.1%
I20569
 
6.6%
19970
 
6.4%
E19719
 
6.4%
S16868
 
5.4%
N15935
 
5.1%
T13658
 
4.4%
D11321
 
3.6%
Other values (28)87449
28.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter289844
93.4%
Space Separator19970
 
6.4%
Dash Punctuation298
 
0.1%
Other Punctuation64
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A51038
17.6%
O28634
 
9.9%
R25015
 
8.6%
I20569
 
7.1%
E19719
 
6.8%
S16868
 
5.8%
N15935
 
5.5%
T13658
 
4.7%
D11321
 
3.9%
U10516
 
3.6%
Other values (25)76571
26.4%
Space Separator
ValueCountFrequency (%)
19970
100.0%
Dash Punctuation
ValueCountFrequency (%)
-298
100.0%
Other Punctuation
ValueCountFrequency (%)
'64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin289844
93.4%
Common20332
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A51038
17.6%
O28634
 
9.9%
R25015
 
8.6%
I20569
 
7.1%
E19719
 
6.8%
S16868
 
5.8%
N15935
 
5.5%
T13658
 
4.7%
D11321
 
3.9%
U10516
 
3.6%
Other values (25)76571
26.4%
Common
ValueCountFrequency (%)
19970
98.2%
-298
 
1.5%
'64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII298557
96.3%
Latin 1 Sup11619
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A51038
17.1%
O28634
 
9.6%
R25015
 
8.4%
I20569
 
6.9%
19970
 
6.7%
E19719
 
6.6%
S16868
 
5.6%
N15935
 
5.3%
T13658
 
4.6%
D11321
 
3.8%
Other values (17)75830
25.4%
Latin 1 Sup
ValueCountFrequency (%)
Ã3726
32.1%
Ó1621
14.0%
É1560
13.4%
Á1493
12.8%
Ç1017
 
8.8%
Í738
 
6.4%
Ú705
 
6.1%
Â450
 
3.9%
Ê213
 
1.8%
Ô74
 
0.6%

CD_CARGO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
13
29269 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters58538
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
1329269
100.0%

Length

2021-07-24T20:06:28.973516image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:29.046578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1329269
100.0%

Most occurring characters

ValueCountFrequency (%)
129269
50.0%
329269
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number58538
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
129269
50.0%
329269
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common58538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
129269
50.0%
329269
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII58538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129269
50.0%
329269
50.0%

DS_CARGO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
VEREADOR
29269 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters234152
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVEREADOR
2nd rowVEREADOR
3rd rowVEREADOR
4th rowVEREADOR
5th rowVEREADOR

Common Values

ValueCountFrequency (%)
VEREADOR29269
100.0%

Length

2021-07-24T20:06:29.225826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:29.311285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
vereador29269
100.0%

Most occurring characters

ValueCountFrequency (%)
E58538
25.0%
R58538
25.0%
V29269
12.5%
A29269
12.5%
D29269
12.5%
O29269
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter234152
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E58538
25.0%
R58538
25.0%
V29269
12.5%
A29269
12.5%
D29269
12.5%
O29269
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin234152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E58538
25.0%
R58538
25.0%
V29269
12.5%
A29269
12.5%
D29269
12.5%
O29269
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII234152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E58538
25.0%
R58538
25.0%
V29269
12.5%
A29269
12.5%
D29269
12.5%
O29269
12.5%

SQ_CANDIDATO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct29269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.797433683 × 1011
Minimum1.700006351 × 1011
Maximum2.000013737 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:29.411051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.700006351 × 1011
5-th percentile1.700006902 × 1011
Q11.700008886 × 1011
median1.7000114 × 1011
Q32.000008369 × 1011
95-th percentile2.000011763 × 1011
Maximum2.000013737 × 1011
Range3.000073861 × 1010
Interquartile range (IQR)2.999994823 × 1010

Descriptive statistics

Standard deviation1.404863704 × 1010
Coefficient of variation (CV)0.07815941791
Kurtosis-1.439789374
Mean1.797433683 × 1011
Median Absolute Deviation (MAD)315058
Skewness0.7485379172
Sum5.260908647 × 1015
Variance1.973642027 × 1020
MonotonicityNot monotonic
2021-07-24T20:06:29.571375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.700010689 × 10111
 
< 0.1%
1.700011737 × 10111
 
< 0.1%
1.700011791 × 10111
 
< 0.1%
1.700012331 × 10111
 
< 0.1%
1.700007093 × 10111
 
< 0.1%
1.700011953 × 10111
 
< 0.1%
1.700009668 × 10111
 
< 0.1%
1.700008069 × 10111
 
< 0.1%
1.700012502 × 10111
 
< 0.1%
1.700010957 × 10111
 
< 0.1%
Other values (29259)29259
> 99.9%
ValueCountFrequency (%)
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006351 × 10111
< 0.1%
1.700006392 × 10111
< 0.1%
ValueCountFrequency (%)
2.000013737 × 10111
< 0.1%
2.000012758 × 10111
< 0.1%
2.000012756 × 10111
< 0.1%
2.000012748 × 10111
< 0.1%
2.000012748 × 10111
< 0.1%
2.000012747 × 10111
< 0.1%
2.000012747 × 10111
< 0.1%
2.000012744 × 10111
< 0.1%
2.000012743 × 10111
< 0.1%
2.000012743 × 10111
< 0.1%

NR_CANDIDATO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4645
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32889.88657
Minimum10000
Maximum90999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:29.725564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10888
Q115123
median23600
Q345333
95-th percentile77321
Maximum90999
Range80999
Interquartile range (IQR)30210

Descriptive statistics

Standard deviation21536.10705
Coefficient of variation (CV)0.6547942025
Kurtosis-0.04359333652
Mean32889.88657
Median Absolute Deviation (MAD)12156
Skewness0.9612749495
Sum962654090
Variance463803906.7
MonotonicityNot monotonic
2021-07-24T20:06:29.888379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000201
 
0.7%
40123196
 
0.7%
40111180
 
0.6%
40222176
 
0.6%
40444174
 
0.6%
15555173
 
0.6%
15000171
 
0.6%
15111167
 
0.6%
15222160
 
0.5%
40333160
 
0.5%
Other values (4635)27511
94.0%
ValueCountFrequency (%)
10000111
0.4%
1000118
 
0.1%
100021
 
< 0.1%
100033
 
< 0.1%
100051
 
< 0.1%
100061
 
< 0.1%
100078
 
< 0.1%
100081
 
< 0.1%
1001022
 
0.1%
100115
 
< 0.1%
ValueCountFrequency (%)
9099940
0.1%
909951
 
< 0.1%
909902
 
< 0.1%
909881
 
< 0.1%
909871
 
< 0.1%
909811
 
< 0.1%
909771
 
< 0.1%
909691
 
< 0.1%
909441
 
< 0.1%
909091
 
< 0.1%

NM_CANDIDATO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct28464
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
JOSÉ CARLOS DA SILVA
 
17
MARIA DE LOURDES DA SILVA
 
15
MARIA JOSÉ DA SILVA
 
15
MARIA APARECIDA DA SILVA
 
11
JOSE CARLOS DA SILVA
 
10
Other values (28459)
29201 

Length

Max length61
Median length25
Mean length26.02603437
Min length6

Characters and Unicode

Total characters761756
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27977 ?
Unique (%)95.6%

Sample

1st rowANDREIA LÚCIA DE FREITAS PEIXOTO
2nd rowMIZAEL JOSÉ DA SILVA
3rd rowCARLOS ANTONIO NOGUEIRA
4th rowJOSÉ SOARES DA SILVA
5th rowMARIA ZULEIDE DOMINGOS

Common Values

ValueCountFrequency (%)
JOSÉ CARLOS DA SILVA17
 
0.1%
MARIA DE LOURDES DA SILVA15
 
0.1%
MARIA JOSÉ DA SILVA15
 
0.1%
MARIA APARECIDA DA SILVA11
 
< 0.1%
JOSE CARLOS DA SILVA10
 
< 0.1%
CARLOS ANTONIO DA SILVA9
 
< 0.1%
FRANCISCO DE ASSIS DA SILVA9
 
< 0.1%
ANA PAULA DA SILVA9
 
< 0.1%
CICERO JOSE DA SILVA8
 
< 0.1%
JOSE ANTONIO DA SILVA8
 
< 0.1%
Other values (28454)29158
99.6%

Length

2021-07-24T20:06:30.239902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silva9849
 
8.1%
de9750
 
8.0%
da9604
 
7.9%
maria3347
 
2.7%
santos2540
 
2.1%
dos2067
 
1.7%
josé2034
 
1.7%
lima1769
 
1.4%
oliveira1769
 
1.4%
jose1749
 
1.4%
Other values (9623)77812
63.6%

Most occurring characters

ValueCountFrequency (%)
A103388
13.6%
93114
12.2%
E65148
8.6%
I63793
8.4%
O60674
8.0%
S53506
 
7.0%
R50864
 
6.7%
L41403
 
5.4%
D40117
 
5.3%
N38459
 
5.0%
Other values (31)151290
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter668641
87.8%
Space Separator93114
 
12.2%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A103388
15.5%
E65148
9.7%
I63793
9.5%
O60674
9.1%
S53506
8.0%
R50864
 
7.6%
L41403
 
6.2%
D40117
 
6.0%
N38459
 
5.8%
V20368
 
3.0%
Other values (29)130921
19.6%
Space Separator
ValueCountFrequency (%)
93114
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin668641
87.8%
Common93115
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A103388
15.5%
E65148
9.7%
I63793
9.5%
O60674
9.1%
S53506
8.0%
R50864
 
7.6%
L41403
 
6.2%
D40117
 
6.0%
N38459
 
5.8%
V20368
 
3.0%
Other values (29)130921
19.6%
Common
ValueCountFrequency (%)
93114
> 99.9%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII754006
99.0%
Latin 1 Sup7750
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A103388
13.7%
93114
12.3%
E65148
8.6%
I63793
8.5%
O60674
8.0%
S53506
 
7.1%
R50864
 
6.7%
L41403
 
5.5%
D40117
 
5.3%
N38459
 
5.1%
Other values (18)143540
19.0%
Latin 1 Sup
ValueCountFrequency (%)
É2769
35.7%
Ã1272
16.4%
Ç902
 
11.6%
Á717
 
9.3%
Ú678
 
8.7%
Í410
 
5.3%
Ô379
 
4.9%
Â234
 
3.0%
Ê181
 
2.3%
Ó171
 
2.2%
Other values (3)37
 
0.5%

NM_URNA_CANDIDATO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct27036
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
ANA PAULA
 
20
MARIA JOSÉ
 
15
ZÉ CARLOS
 
14
SIMONE
 
13
MARQUINHOS
 
11
Other values (27031)
29196 

Length

Max length30
Median length14
Mean length13.70590044
Min length2

Characters and Unicode

Total characters401158
Distinct characters57
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25677 ?
Unique (%)87.7%

Sample

1st rowANDREIA DE JUNIOR
2nd rowMIZAEL DO POVO
3rd rowCARLINHOS DE CIBIU
4th rowJUCA SOARES
5th rowCÊ DE ASSIS VAQUEIRO

Common Values

ValueCountFrequency (%)
ANA PAULA20
 
0.1%
MARIA JOSÉ15
 
0.1%
ZÉ CARLOS14
 
< 0.1%
SIMONE13
 
< 0.1%
MARQUINHOS11
 
< 0.1%
NEIDE11
 
< 0.1%
PAULINHO10
 
< 0.1%
JOÃO PAULO10
 
< 0.1%
ADRIANA10
 
< 0.1%
CARLINHOS9
 
< 0.1%
Other values (27026)29146
99.6%

Length

2021-07-24T20:06:30.578328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de3268
 
5.0%
do2608
 
4.0%
da2355
 
3.6%
irmão624
 
0.9%
maria463
 
0.7%
professor419
 
0.6%
397
 
0.6%
silva388
 
0.6%
joão356
 
0.5%
professora352
 
0.5%
Other values (13086)54598
82.9%

Most occurring characters

ValueCountFrequency (%)
A51488
12.8%
O37507
 
9.3%
36870
 
9.2%
E33604
 
8.4%
I32775
 
8.2%
R28715
 
7.2%
N23906
 
6.0%
D21544
 
5.4%
L19556
 
4.9%
S18396
 
4.6%
Other values (47)96797
24.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter364090
90.8%
Space Separator36870
 
9.2%
Other Punctuation126
 
< 0.1%
Decimal Number25
 
< 0.1%
Dash Punctuation19
 
< 0.1%
Open Punctuation12
 
< 0.1%
Close Punctuation12
 
< 0.1%
Other Symbol2
 
< 0.1%
Modifier Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A51488
14.1%
O37507
10.3%
E33604
 
9.2%
I32775
 
9.0%
R28715
 
7.9%
N23906
 
6.6%
D21544
 
5.9%
L19556
 
5.4%
S18396
 
5.1%
C11796
 
3.2%
Other values (30)84803
23.3%
Decimal Number
ValueCountFrequency (%)
17
28.0%
05
20.0%
23
12.0%
53
12.0%
83
12.0%
32
 
8.0%
41
 
4.0%
61
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.123
97.6%
,2
 
1.6%
/1
 
0.8%
Space Separator
ValueCountFrequency (%)
36870
100.0%
Open Punctuation
ValueCountFrequency (%)
(12
100.0%
Close Punctuation
ValueCountFrequency (%)
)12
100.0%
Dash Punctuation
ValueCountFrequency (%)
-19
100.0%
Other Symbol
ValueCountFrequency (%)
°2
100.0%
Modifier Symbol
ValueCountFrequency (%)
´2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin364090
90.8%
Common37068
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A51488
14.1%
O37507
10.3%
E33604
 
9.2%
I32775
 
9.0%
R28715
 
7.9%
N23906
 
6.6%
D21544
 
5.9%
L19556
 
5.4%
S18396
 
5.1%
C11796
 
3.2%
Other values (30)84803
23.3%
Common
ValueCountFrequency (%)
36870
99.5%
.123
 
0.3%
-19
 
0.1%
(12
 
< 0.1%
)12
 
< 0.1%
17
 
< 0.1%
05
 
< 0.1%
23
 
< 0.1%
53
 
< 0.1%
83
 
< 0.1%
Other values (7)11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII393033
98.0%
Latin 1 Sup8125
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A51488
13.1%
O37507
9.5%
36870
9.4%
E33604
 
8.5%
I32775
 
8.3%
R28715
 
7.3%
N23906
 
6.1%
D21544
 
5.5%
L19556
 
5.0%
S18396
 
4.7%
Other values (31)88672
22.6%
Latin 1 Sup
ValueCountFrequency (%)
Ã2430
29.9%
É1629
20.0%
Á1078
13.3%
Ç689
 
8.5%
Ú688
 
8.5%
Í403
 
5.0%
Ó344
 
4.2%
Ô310
 
3.8%
Â253
 
3.1%
Ê234
 
2.9%
Other values (6)67
 
0.8%

NM_SOCIAL_CANDIDATO
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
#NULO#
29256 
GENILDI DE LIMA
 
1
ROBERTTA LLEONY
 
1
PAULLA BLADHY PAULINO DA SILVA
 
1
SOFIA FRAGOSO DA SILVA
 
1
Other values (9)
 
9

Length

Max length34
Median length6
Mean length6.007379822
Min length6

Characters and Unicode

Total characters175830
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row#NULO#
2nd row#NULO#
3rd row#NULO#
4th row#NULO#
5th row#NULO#

Common Values

ValueCountFrequency (%)
#NULO#29256
> 99.9%
GENILDI DE LIMA1
 
< 0.1%
ROBERTTA LLEONY1
 
< 0.1%
PAULLA BLADHY PAULINO DA SILVA1
 
< 0.1%
SOFIA FRAGOSO DA SILVA1
 
< 0.1%
BRUNA RAFAELA NASCIMENTO1
 
< 0.1%
ADRIANO DINIZ1
 
< 0.1%
ADRYELLY FERRAZ1
 
< 0.1%
MICHELY BARBOSA DE PONTES1
 
< 0.1%
LUDMILA FELIX DE ANDRADE1
 
< 0.1%
Other values (4)4
 
< 0.1%

Length

2021-07-24T20:06:30.846789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nulo29256
99.8%
da4
 
< 0.1%
de4
 
< 0.1%
silva3
 
< 0.1%
medeiros2
 
< 0.1%
santos1
 
< 0.1%
dos1
 
< 0.1%
moises1
 
< 0.1%
wanessa1
 
< 0.1%
almeida1
 
< 0.1%
Other values (28)28
 
0.1%

Most occurring characters

ValueCountFrequency (%)
#58512
33.3%
L29281
16.7%
O29273
16.6%
N29270
16.6%
U29263
16.6%
A42
 
< 0.1%
33
 
< 0.1%
E25
 
< 0.1%
I22
 
< 0.1%
D22
 
< 0.1%
Other values (16)87
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter117285
66.7%
Other Punctuation58512
33.3%
Space Separator33
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L29281
25.0%
O29273
25.0%
N29270
25.0%
U29263
25.0%
A42
 
< 0.1%
E25
 
< 0.1%
I22
 
< 0.1%
D22
 
< 0.1%
S21
 
< 0.1%
R15
 
< 0.1%
Other values (14)51
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
#58512
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin117285
66.7%
Common58545
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L29281
25.0%
O29273
25.0%
N29270
25.0%
U29263
25.0%
A42
 
< 0.1%
E25
 
< 0.1%
I22
 
< 0.1%
D22
 
< 0.1%
S21
 
< 0.1%
R15
 
< 0.1%
Other values (14)51
 
< 0.1%
Common
ValueCountFrequency (%)
#58512
99.9%
33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII175830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
#58512
33.3%
L29281
16.7%
O29273
16.6%
N29270
16.6%
U29263
16.6%
A42
 
< 0.1%
33
 
< 0.1%
E25
 
< 0.1%
I22
 
< 0.1%
D22
 
< 0.1%
Other values (16)87
 
< 0.1%

NR_CPF_CANDIDATO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct29248
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.922522645 × 1010
Minimum13219430
Maximum9.997776143 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:31.011192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum13219430
5-th percentile1354546855
Q14714564463
median1.015996044 × 1010
Q35.514076947 × 1010
95-th percentile8.870938189 × 1010
Maximum9.997776143 × 1010
Range9.9964542 × 1010
Interquartile range (IQR)5.042620500 × 1010

Descriptive statistics

Standard deviation3.108039718 × 1010
Coefficient of variation (CV)1.063478404
Kurtosis-0.8152096364
Mean2.922522645 × 1010
Median Absolute Deviation (MAD)7960146984
Skewness0.857047538
Sum8.55393153 × 1014
Variance9.659910891 × 1020
MonotonicityNot monotonic
2021-07-24T20:06:31.209097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85144224212
 
< 0.1%
69803524072
 
< 0.1%
23552864182
 
< 0.1%
61306914592
 
< 0.1%
61701004862
 
< 0.1%
39625454332
 
< 0.1%
7.328183277 × 10102
 
< 0.1%
1.282265946 × 10102
 
< 0.1%
9.26459767 × 10102
 
< 0.1%
4.54705964 × 10102
 
< 0.1%
Other values (29238)29249
99.9%
ValueCountFrequency (%)
132194301
< 0.1%
266645691
< 0.1%
270464191
< 0.1%
270674161
< 0.1%
284733701
< 0.1%
288534401
< 0.1%
330524411
< 0.1%
346294321
< 0.1%
348677081
< 0.1%
368784801
< 0.1%
ValueCountFrequency (%)
9.997776143 × 10101
< 0.1%
9.996920349 × 10101
< 0.1%
9.996481387 × 10101
< 0.1%
9.996365549 × 10101
< 0.1%
9.994448013 × 10101
< 0.1%
9.993695041 × 10101
< 0.1%
9.988964145 × 10101
< 0.1%
9.988335041 × 10101
< 0.1%
9.988298145 × 10101
< 0.1%
9.987155642 × 10101
< 0.1%

NM_EMAIL
Categorical

HIGH CARDINALITY

Distinct13384
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PABLO.ELEITORAL2020@GMAIL.COM
 
352
PTCESTADUALPE@GMAIL.COM
 
160
MARC_GUEDES@HOTMAIL.COM
 
137
MVCONTABIL1969@HOTMAIL.COM
 
113
FRENTEPOPULARCABO@GMAIL.COM
 
104
Other values (13379)
28403 

Length

Max length42
Median length25
Mean length25.86538659
Min length14

Characters and Unicode

Total characters757054
Distinct characters44
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11986 ?
Unique (%)41.0%

Sample

1st rowANDREIAFREITAS0405@HOTMAIL.COM
2nd rowFRENTEPOPULARCABO@GMAIL.COM
3rd rowPRISCILLAFILO2020@GMAIL.COM
4th rowPTCESTADUALPE@GMAIL.COM
5th rowRCA109@HOTMAIL.COM

Common Values

ValueCountFrequency (%)
PABLO.ELEITORAL2020@GMAIL.COM352
 
1.2%
PTCESTADUALPE@GMAIL.COM160
 
0.5%
MARC_GUEDES@HOTMAIL.COM137
 
0.5%
MVCONTABIL1969@HOTMAIL.COM113
 
0.4%
FRENTEPOPULARCABO@GMAIL.COM104
 
0.4%
ELEICOES4040@HOTMAIL.COM102
 
0.3%
LNLPROCESSOS@GMAIL.COM100
 
0.3%
OTRABALHOCONTINUA.2020@GMAIL.COM99
 
0.3%
SLMELEICAO2020@GMAIL.COM96
 
0.3%
COMERCIAL@CONTAFACILCONSULTORIA.COM.BR90
 
0.3%
Other values (13374)27916
95.4%

Length

2021-07-24T20:06:31.562398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pablo.eleitoral2020@gmail.com352
 
1.2%
ptcestadualpe@gmail.com160
 
0.5%
marc_guedes@hotmail.com137
 
0.5%
mvcontabil1969@hotmail.com113
 
0.4%
frentepopularcabo@gmail.com104
 
0.4%
eleicoes4040@hotmail.com102
 
0.3%
lnlprocessos@gmail.com100
 
0.3%
otrabalhocontinua.2020@gmail.com99
 
0.3%
slmeleicao2020@gmail.com96
 
0.3%
comercial@contafacilconsultoria.com.br90
 
0.3%
Other values (13374)27916
95.4%

Most occurring characters

ValueCountFrequency (%)
A84970
 
11.2%
O80226
 
10.6%
M68282
 
9.0%
I60239
 
8.0%
L50604
 
6.7%
C47535
 
6.3%
E38054
 
5.0%
.36047
 
4.8%
R32765
 
4.3%
@29269
 
3.9%
Other values (34)229063
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter650866
86.0%
Other Punctuation65320
 
8.6%
Decimal Number39037
 
5.2%
Connector Punctuation1443
 
0.2%
Dash Punctuation386
 
0.1%
Math Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A84970
13.1%
O80226
12.3%
M68282
10.5%
I60239
9.3%
L50604
 
7.8%
C47535
 
7.3%
E38054
 
5.8%
R32765
 
5.0%
G25068
 
3.9%
S23976
 
3.7%
Other values (16)139147
21.4%
Decimal Number
ValueCountFrequency (%)
010257
26.3%
29702
24.9%
15397
13.8%
52543
 
6.5%
42259
 
5.8%
92097
 
5.4%
71904
 
4.9%
31892
 
4.8%
81527
 
3.9%
61459
 
3.7%
Other Punctuation
ValueCountFrequency (%)
.36047
55.2%
@29269
44.8%
'3
 
< 0.1%
!1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
|1
50.0%
~1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_1443
100.0%
Dash Punctuation
ValueCountFrequency (%)
-386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin650866
86.0%
Common106188
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A84970
13.1%
O80226
12.3%
M68282
10.5%
I60239
9.3%
L50604
 
7.8%
C47535
 
7.3%
E38054
 
5.8%
R32765
 
5.0%
G25068
 
3.9%
S23976
 
3.7%
Other values (16)139147
21.4%
Common
ValueCountFrequency (%)
.36047
33.9%
@29269
27.6%
010257
 
9.7%
29702
 
9.1%
15397
 
5.1%
52543
 
2.4%
42259
 
2.1%
92097
 
2.0%
71904
 
1.8%
31892
 
1.8%
Other values (8)4821
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII757054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A84970
 
11.2%
O80226
 
10.6%
M68282
 
9.0%
I60239
 
8.0%
L50604
 
6.7%
C47535
 
6.3%
E38054
 
5.0%
.36047
 
4.8%
R32765
 
4.3%
@29269
 
3.9%
Other values (34)229063
30.3%

CD_SITUACAO_CANDIDATURA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
12
27839 
3
 
1430

Length

Max length2
Median length2
Mean length1.951142847
Min length1

Characters and Unicode

Total characters57108
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
1227839
95.1%
31430
 
4.9%

Length

2021-07-24T20:06:31.796010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:31.886268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1227839
95.1%
31430
 
4.9%

Most occurring characters

ValueCountFrequency (%)
127839
48.7%
227839
48.7%
31430
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number57108
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
127839
48.7%
227839
48.7%
31430
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common57108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
127839
48.7%
227839
48.7%
31430
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII57108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127839
48.7%
227839
48.7%
31430
 
2.5%

DS_SITUACAO_CANDIDATURA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
APTO
27839 
INAPTO
 
1430

Length

Max length6
Median length4
Mean length4.097714305
Min length4

Characters and Unicode

Total characters119936
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAPTO
2nd rowAPTO
3rd rowAPTO
4th rowAPTO
5th rowAPTO

Common Values

ValueCountFrequency (%)
APTO27839
95.1%
INAPTO1430
 
4.9%

Length

2021-07-24T20:06:32.087337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:32.172160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
apto27839
95.1%
inapto1430
 
4.9%

Most occurring characters

ValueCountFrequency (%)
A29269
24.4%
P29269
24.4%
T29269
24.4%
O29269
24.4%
I1430
 
1.2%
N1430
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter119936
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A29269
24.4%
P29269
24.4%
T29269
24.4%
O29269
24.4%
I1430
 
1.2%
N1430
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin119936
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A29269
24.4%
P29269
24.4%
T29269
24.4%
O29269
24.4%
I1430
 
1.2%
N1430
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII119936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A29269
24.4%
P29269
24.4%
T29269
24.4%
O29269
24.4%
I1430
 
1.2%
N1430
 
1.2%

CD_DETALHE_SITUACAO_CAND
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.441012676
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:32.243754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile4
Maximum16
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.111208822
Coefficient of variation (CV)0.864890561
Kurtosis24.35584115
Mean2.441012676
Median Absolute Deviation (MAD)0
Skewness5.038318842
Sum71446
Variance4.457202692
MonotonicityNot monotonic
2021-07-24T20:06:32.338025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
227781
94.9%
14853
 
2.9%
6550
 
1.9%
446
 
0.2%
1314
 
< 0.1%
1612
 
< 0.1%
77
 
< 0.1%
55
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
227781
94.9%
446
 
0.2%
55
 
< 0.1%
6550
 
1.9%
77
 
< 0.1%
101
 
< 0.1%
1314
 
< 0.1%
14853
 
2.9%
1612
 
< 0.1%
ValueCountFrequency (%)
1612
 
< 0.1%
14853
 
2.9%
1314
 
< 0.1%
101
 
< 0.1%
77
 
< 0.1%
6550
 
1.9%
55
 
< 0.1%
446
 
0.2%
227781
94.9%

DS_DETALHE_SITUACAO_CAND
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
DEFERIDO
27781 
INDEFERIDO
 
853
RENÚNCIA
 
550
INDEFERIDO COM RECURSO
 
46
PEDIDO NÃO CONHECIDO
 
14
Other values (4)
 
25

Length

Max length22
Median length8
Mean length8.091086132
Min length7

Characters and Unicode

Total characters236818
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO27781
94.9%
INDEFERIDO853
 
2.9%
RENÚNCIA550
 
1.9%
INDEFERIDO COM RECURSO46
 
0.2%
PEDIDO NÃO CONHECIDO14
 
< 0.1%
DEFERIDO COM RECURSO12
 
< 0.1%
FALECIDO7
 
< 0.1%
CANCELADO5
 
< 0.1%
CASSADO1
 
< 0.1%

Length

2021-07-24T20:06:33.166884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:33.264078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
deferido27793
94.5%
indeferido899
 
3.1%
renúncia550
 
1.9%
com58
 
0.2%
recurso58
 
0.2%
pedido14
 
< 0.1%
não14
 
< 0.1%
conhecido14
 
< 0.1%
falecido7
 
< 0.1%
cancelado5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E58032
24.5%
D57439
24.3%
I30176
12.7%
R29358
12.4%
O28877
12.2%
F28699
12.1%
N2032
 
0.9%
C712
 
0.3%
A569
 
0.2%
Ú550
 
0.2%
Other values (8)374
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter236674
99.9%
Space Separator144
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E58032
24.5%
D57439
24.3%
I30176
12.8%
R29358
12.4%
O28877
12.2%
F28699
12.1%
N2032
 
0.9%
C712
 
0.3%
A569
 
0.2%
Ú550
 
0.2%
Other values (7)230
 
0.1%
Space Separator
ValueCountFrequency (%)
144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin236674
99.9%
Common144
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E58032
24.5%
D57439
24.3%
I30176
12.8%
R29358
12.4%
O28877
12.2%
F28699
12.1%
N2032
 
0.9%
C712
 
0.3%
A569
 
0.2%
Ú550
 
0.2%
Other values (7)230
 
0.1%
Common
ValueCountFrequency (%)
144
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII236254
99.8%
Latin 1 Sup564
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E58032
24.6%
D57439
24.3%
I30176
12.8%
R29358
12.4%
O28877
12.2%
F28699
12.1%
N2032
 
0.9%
C712
 
0.3%
A569
 
0.2%
144
 
0.1%
Other values (6)216
 
0.1%
Latin 1 Sup
ValueCountFrequency (%)
Ú550
97.5%
Ã14
 
2.5%

TP_AGREMIACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PARTIDO ISOLADO
29269 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters439035
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowPARTIDO ISOLADO
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO29269
100.0%

Length

2021-07-24T20:06:33.444959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:33.506157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
partido29269
50.0%
isolado29269
50.0%

Most occurring characters

ValueCountFrequency (%)
O87807
20.0%
A58538
13.3%
I58538
13.3%
D58538
13.3%
P29269
 
6.7%
R29269
 
6.7%
T29269
 
6.7%
29269
 
6.7%
S29269
 
6.7%
L29269
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter409766
93.3%
Space Separator29269
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O87807
21.4%
A58538
14.3%
I58538
14.3%
D58538
14.3%
P29269
 
7.1%
R29269
 
7.1%
T29269
 
7.1%
S29269
 
7.1%
L29269
 
7.1%
Space Separator
ValueCountFrequency (%)
29269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin409766
93.3%
Common29269
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O87807
21.4%
A58538
14.3%
I58538
14.3%
D58538
14.3%
P29269
 
7.1%
R29269
 
7.1%
T29269
 
7.1%
S29269
 
7.1%
L29269
 
7.1%
Common
ValueCountFrequency (%)
29269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII439035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O87807
20.0%
A58538
13.3%
I58538
13.3%
D58538
13.3%
P29269
 
6.7%
R29269
 
6.7%
T29269
 
6.7%
29269
 
6.7%
S29269
 
6.7%
L29269
 
6.7%

NR_PARTIDO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.51378592
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:33.579445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q115
median23
Q345
95-th percentile77
Maximum90
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.52518357
Coefficient of variation (CV)0.6620325183
Kurtosis-0.04382616724
Mean32.51378592
Median Absolute Deviation (MAD)12
Skewness0.9614049376
Sum951646
Variance463.3335277
MonotonicityNot monotonic
2021-07-24T20:06:33.734335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
402800
 
9.6%
152499
 
8.5%
111801
 
6.2%
551753
 
6.0%
131605
 
5.5%
101569
 
5.4%
221431
 
4.9%
451426
 
4.9%
251419
 
4.8%
171197
 
4.1%
Other values (23)11769
40.2%
ValueCountFrequency (%)
101569
5.4%
111801
6.2%
12913
 
3.1%
131605
5.5%
14827
 
2.8%
152499
8.5%
1610
 
< 0.1%
171197
4.1%
18218
 
0.7%
19761
 
2.6%
ValueCountFrequency (%)
90817
2.8%
8022
 
0.1%
771094
3.7%
701040
3.6%
65824
2.8%
551753
6.0%
51576
 
2.0%
50415
 
1.4%
451426
4.9%
43530
 
1.8%

SG_PARTIDO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PSB
2800 
MDB
2499 
PP
 
1801
PSD
 
1753
PT
 
1605
Other values (28)
18811 

Length

Max length13
Median length3
Mean length4.299531928
Min length2

Characters and Unicode

Total characters125843
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPDT
3rd rowPSB
4th rowPTC
5th rowPSD

Common Values

ValueCountFrequency (%)
PSB2800
 
9.6%
MDB2499
 
8.5%
PP1801
 
6.2%
PSD1753
 
6.0%
PT1605
 
5.5%
REPUBLICANOS1569
 
5.4%
PL1431
 
4.9%
PSDB1426
 
4.9%
DEM1419
 
4.8%
PSL1197
 
4.1%
Other values (23)11769
40.2%

Length

2021-07-24T20:06:34.105783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psb2800
 
9.1%
mdb2499
 
8.1%
pp1801
 
5.8%
psd1753
 
5.7%
pt1605
 
5.2%
republicanos1569
 
5.1%
pl1431
 
4.6%
psdb1426
 
4.6%
dem1419
 
4.6%
psl1197
 
3.9%
Other values (25)13417
43.4%

Most occurring characters

ValueCountFrequency (%)
P23732
18.9%
D14163
11.3%
S12213
9.7%
B10613
8.4%
A9536
 
7.6%
E7413
 
5.9%
T6657
 
5.3%
I6031
 
4.8%
L5706
 
4.5%
O5284
 
4.2%
Other values (9)24495
19.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter122547
97.4%
Space Separator1648
 
1.3%
Lowercase Letter1648
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P23732
19.4%
D14163
11.6%
S12213
10.0%
B10613
8.7%
A9536
7.8%
E7413
 
6.0%
T6657
 
5.4%
I6031
 
4.9%
L5706
 
4.7%
O5284
 
4.3%
Other values (6)21199
17.3%
Lowercase Letter
ValueCountFrequency (%)
d824
50.0%
o824
50.0%
Space Separator
ValueCountFrequency (%)
1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124195
98.7%
Common1648
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P23732
19.1%
D14163
11.4%
S12213
9.8%
B10613
8.5%
A9536
7.7%
E7413
 
6.0%
T6657
 
5.4%
I6031
 
4.9%
L5706
 
4.6%
O5284
 
4.3%
Other values (8)22847
18.4%
Common
ValueCountFrequency (%)
1648
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125843
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P23732
18.9%
D14163
11.3%
S12213
9.7%
B10613
8.4%
A9536
 
7.6%
E7413
 
5.9%
T6657
 
5.3%
I6031
 
4.8%
L5706
 
4.5%
O5284
 
4.2%
Other values (9)24495
19.5%

NM_PARTIDO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PARTIDO SOCIALISTA BRASILEIRO
2800 
MOVIMENTO DEMOCRÁTICO BRASILEIRO
2499 
PROGRESSISTAS
 
1801
PARTIDO SOCIAL DEMOCRÁTICO
 
1753
PARTIDO DOS TRABALHADORES
 
1605
Other values (28)
18811 

Length

Max length46
Median length25
Mean length22.227647
Min length6

Characters and Unicode

Total characters650581
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPROGRESSISTAS
2nd rowPARTIDO DEMOCRÁTICO TRABALHISTA
3rd rowPARTIDO SOCIALISTA BRASILEIRO
4th rowPARTIDO TRABALHISTA CRISTÃO
5th rowPARTIDO SOCIAL DEMOCRÁTICO

Common Values

ValueCountFrequency (%)
PARTIDO SOCIALISTA BRASILEIRO2800
 
9.6%
MOVIMENTO DEMOCRÁTICO BRASILEIRO2499
 
8.5%
PROGRESSISTAS1801
 
6.2%
PARTIDO SOCIAL DEMOCRÁTICO1753
 
6.0%
PARTIDO DOS TRABALHADORES1605
 
5.5%
REPUBLICANOS1569
 
5.4%
PARTIDO LIBERAL1431
 
4.9%
PARTIDO DA SOCIAL DEMOCRACIA BRASILEIRA1426
 
4.9%
DEMOCRATAS1419
 
4.8%
PARTIDO SOCIAL LIBERAL1197
 
4.1%
Other values (23)11769
40.2%

Length

2021-07-24T20:06:34.437539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partido17227
23.3%
brasileiro6626
 
9.0%
social6325
 
8.6%
democrático5165
 
7.0%
trabalhista2850
 
3.9%
socialista2810
 
3.8%
da2651
 
3.6%
liberal2628
 
3.6%
movimento2499
 
3.4%
progressistas1801
 
2.4%
Other values (34)23236
31.5%

Most occurring characters

ValueCountFrequency (%)
A80080
12.3%
I75054
11.5%
O68799
10.6%
R64167
9.9%
44549
 
6.8%
T43632
 
6.7%
S43152
 
6.6%
D41258
 
6.3%
E36024
 
5.5%
L33094
 
5.1%
Other values (14)120772
18.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter606032
93.2%
Space Separator44549
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A80080
13.2%
I75054
12.4%
O68799
11.4%
R64167
10.6%
T43632
7.2%
S43152
7.1%
D41258
6.8%
E36024
5.9%
L33094
 
5.5%
C30792
 
5.1%
Other values (13)89980
14.8%
Space Separator
ValueCountFrequency (%)
44549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin606032
93.2%
Common44549
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A80080
13.2%
I75054
12.4%
O68799
11.4%
R64167
10.6%
T43632
7.2%
S43152
7.1%
D41258
6.8%
E36024
5.9%
L33094
 
5.5%
C30792
 
5.1%
Other values (13)89980
14.8%
Common
ValueCountFrequency (%)
44549
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII642998
98.8%
Latin 1 Sup7583
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A80080
12.5%
I75054
11.7%
O68799
10.7%
R64167
10.0%
44549
 
6.9%
T43632
 
6.8%
S43152
 
6.7%
D41258
 
6.4%
E36024
 
5.6%
L33094
 
5.1%
Other values (11)113189
17.6%
Latin 1 Sup
ValueCountFrequency (%)
Á5167
68.1%
Ã2178
28.7%
Ç238
 
3.1%

SQ_COLIGACAO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2195
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.797425012 × 1011
Minimum1.700000525 × 1011
Maximum2.00000179 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:34.594546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.700000525 × 1011
5-th percentile1.700000637 × 1011
Q11.700000996 × 1011
median1.700001377 × 1011
Q32.000000879 × 1011
95-th percentile2.000001439 × 1011
Maximum2.00000179 × 1011
Range3.000012653 × 1010
Interquartile range (IQR)2.999998833 × 1010

Descriptive statistics

Standard deviation1.404863938 × 1010
Coefficient of variation (CV)0.07815980795
Kurtosis-1.439789374
Mean1.797425012 × 1011
Median Absolute Deviation (MAD)51680
Skewness0.7485379174
Sum5.260883269 × 1015
Variance1.973642683 × 1020
MonotonicityNot monotonic
2021-07-24T20:06:34.755174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.700001356 × 101159
 
0.2%
1.700001499 × 101159
 
0.2%
1.700000886 × 101158
 
0.2%
1.700001552 × 101157
 
0.2%
1.700000888 × 101156
 
0.2%
1.700000888 × 101153
 
0.2%
1.700001551 × 101152
 
0.2%
1.700001073 × 101151
 
0.2%
1.700000892 × 101150
 
0.2%
1.700001464 × 101145
 
0.2%
Other values (2185)28729
98.2%
ValueCountFrequency (%)
1.700000525 × 10119
< 0.1%
1.700000559 × 10118
 
< 0.1%
1.700000562 × 10119
< 0.1%
1.700000563 × 101113
< 0.1%
1.700000564 × 10111
 
< 0.1%
1.700000567 × 101110
< 0.1%
1.700000568 × 101121
0.1%
1.700000568 × 10111
 
< 0.1%
1.700000568 × 101116
0.1%
1.700000568 × 101112
< 0.1%
ValueCountFrequency (%)
2.00000179 × 10112
 
< 0.1%
2.000001747 × 10119
 
< 0.1%
2.0000017 × 10111
 
< 0.1%
2.000001636 × 101113
 
< 0.1%
2.000001613 × 101117
0.1%
2.000001613 × 101120
0.1%
2.00000161 × 101123
0.1%
2.00000161 × 101137
0.1%
2.000001609 × 101114
 
< 0.1%
2.000001589 × 101112
 
< 0.1%

NM_COLIGACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PARTIDO ISOLADO
29269 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters439035
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARTIDO ISOLADO
2nd rowPARTIDO ISOLADO
3rd rowPARTIDO ISOLADO
4th rowPARTIDO ISOLADO
5th rowPARTIDO ISOLADO

Common Values

ValueCountFrequency (%)
PARTIDO ISOLADO29269
100.0%

Length

2021-07-24T20:06:34.993651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:35.068686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
partido29269
50.0%
isolado29269
50.0%

Most occurring characters

ValueCountFrequency (%)
O87807
20.0%
A58538
13.3%
I58538
13.3%
D58538
13.3%
P29269
 
6.7%
R29269
 
6.7%
T29269
 
6.7%
29269
 
6.7%
S29269
 
6.7%
L29269
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter409766
93.3%
Space Separator29269
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O87807
21.4%
A58538
14.3%
I58538
14.3%
D58538
14.3%
P29269
 
7.1%
R29269
 
7.1%
T29269
 
7.1%
S29269
 
7.1%
L29269
 
7.1%
Space Separator
ValueCountFrequency (%)
29269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin409766
93.3%
Common29269
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O87807
21.4%
A58538
14.3%
I58538
14.3%
D58538
14.3%
P29269
 
7.1%
R29269
 
7.1%
T29269
 
7.1%
S29269
 
7.1%
L29269
 
7.1%
Common
ValueCountFrequency (%)
29269
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII439035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O87807
20.0%
A58538
13.3%
I58538
13.3%
D58538
13.3%
P29269
 
6.7%
R29269
 
6.7%
T29269
 
6.7%
29269
 
6.7%
S29269
 
6.7%
L29269
 
6.7%

DS_COMPOSICAO_COLIGACAO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PSB
2800 
MDB
2499 
PP
 
1801
PSD
 
1753
PT
 
1605
Other values (28)
18811 

Length

Max length13
Median length3
Mean length4.299531928
Min length2

Characters and Unicode

Total characters125843
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPP
2nd rowPDT
3rd rowPSB
4th rowPTC
5th rowPSD

Common Values

ValueCountFrequency (%)
PSB2800
 
9.6%
MDB2499
 
8.5%
PP1801
 
6.2%
PSD1753
 
6.0%
PT1605
 
5.5%
REPUBLICANOS1569
 
5.4%
PL1431
 
4.9%
PSDB1426
 
4.9%
DEM1419
 
4.8%
PSL1197
 
4.1%
Other values (23)11769
40.2%

Length

2021-07-24T20:06:35.288731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
psb2800
 
9.1%
mdb2499
 
8.1%
pp1801
 
5.8%
psd1753
 
5.7%
pt1605
 
5.2%
republicanos1569
 
5.1%
pl1431
 
4.6%
psdb1426
 
4.6%
dem1419
 
4.6%
psl1197
 
3.9%
Other values (25)13417
43.4%

Most occurring characters

ValueCountFrequency (%)
P23732
18.9%
D14163
11.3%
S12213
9.7%
B10613
8.4%
A9536
 
7.6%
E7413
 
5.9%
T6657
 
5.3%
I6031
 
4.8%
L5706
 
4.5%
O5284
 
4.2%
Other values (9)24495
19.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter122547
97.4%
Space Separator1648
 
1.3%
Lowercase Letter1648
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P23732
19.4%
D14163
11.6%
S12213
10.0%
B10613
8.7%
A9536
7.8%
E7413
 
6.0%
T6657
 
5.4%
I6031
 
4.9%
L5706
 
4.7%
O5284
 
4.3%
Other values (6)21199
17.3%
Lowercase Letter
ValueCountFrequency (%)
d824
50.0%
o824
50.0%
Space Separator
ValueCountFrequency (%)
1648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124195
98.7%
Common1648
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P23732
19.1%
D14163
11.4%
S12213
9.8%
B10613
8.5%
A9536
7.7%
E7413
 
6.0%
T6657
 
5.4%
I6031
 
4.9%
L5706
 
4.6%
O5284
 
4.3%
Other values (8)22847
18.4%
Common
ValueCountFrequency (%)
1648
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII125843
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P23732
18.9%
D14163
11.3%
S12213
9.7%
B10613
8.4%
A9536
 
7.6%
E7413
 
5.9%
T6657
 
5.3%
I6031
 
4.8%
L5706
 
4.5%
O5284
 
4.2%
Other values (9)24495
19.5%

CD_NACIONALIDADE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
1
29090 
2
 
178
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

Length

2021-07-24T20:06:35.537782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:35.631062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common29269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129090
99.4%
2178
 
0.6%
41
 
< 0.1%

DS_NACIONALIDADE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
BRASILEIRA NATA
29090 
BRASILEIRA (NATURALIZADA)
 
178
ESTRANGEIRO
 
1

Length

Max length25
Median length15
Mean length15.06067853
Min length11

Characters and Unicode

Total characters440811
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBRASILEIRA NATA
2nd rowBRASILEIRA NATA
3rd rowBRASILEIRA NATA
4th rowBRASILEIRA NATA
5th rowBRASILEIRA NATA

Common Values

ValueCountFrequency (%)
BRASILEIRA NATA29090
99.4%
BRASILEIRA (NATURALIZADA)178
 
0.6%
ESTRANGEIRO1
 
< 0.1%

Length

2021-07-24T20:06:35.871995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:36.001997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
brasileira29268
50.0%
nata29090
49.7%
naturalizada178
 
0.3%
estrangeiro1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A117429
26.6%
R58716
13.3%
I58715
13.3%
L29446
 
6.7%
E29270
 
6.6%
S29269
 
6.6%
N29269
 
6.6%
T29269
 
6.6%
B29268
 
6.6%
29268
 
6.6%
Other values (7)892
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter411187
93.3%
Space Separator29268
 
6.6%
Open Punctuation178
 
< 0.1%
Close Punctuation178
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A117429
28.6%
R58716
14.3%
I58715
14.3%
L29446
 
7.2%
E29270
 
7.1%
S29269
 
7.1%
N29269
 
7.1%
T29269
 
7.1%
B29268
 
7.1%
U178
 
< 0.1%
Other values (4)358
 
0.1%
Space Separator
ValueCountFrequency (%)
29268
100.0%
Open Punctuation
ValueCountFrequency (%)
(178
100.0%
Close Punctuation
ValueCountFrequency (%)
)178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin411187
93.3%
Common29624
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A117429
28.6%
R58716
14.3%
I58715
14.3%
L29446
 
7.2%
E29270
 
7.1%
S29269
 
7.1%
N29269
 
7.1%
T29269
 
7.1%
B29268
 
7.1%
U178
 
< 0.1%
Other values (4)358
 
0.1%
Common
ValueCountFrequency (%)
29268
98.8%
(178
 
0.6%
)178
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII440811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A117429
26.6%
R58716
13.3%
I58715
13.3%
L29446
 
6.7%
E29270
 
6.6%
S29269
 
6.6%
N29269
 
6.6%
T29269
 
6.6%
B29268
 
6.6%
29268
 
6.6%
Other values (7)892
 
0.2%

SG_UF_NASCIMENTO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PE
18436 
RN
8711 
PB
 
570
SP
 
401
BA
 
242
Other values (23)
 
909

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters58538
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPE
2nd rowPE
3rd rowPE
4th rowPE
5th rowPE

Common Values

ValueCountFrequency (%)
PE18436
63.0%
RN8711
29.8%
PB570
 
1.9%
SP401
 
1.4%
BA242
 
0.8%
CE234
 
0.8%
AL217
 
0.7%
RJ175
 
0.6%
PI51
 
0.2%
MG42
 
0.1%
Other values (18)190
 
0.6%

Length

2021-07-24T20:06:36.225727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pe18436
63.0%
rn8711
29.8%
pb570
 
1.9%
sp401
 
1.4%
ba242
 
0.8%
ce234
 
0.8%
al217
 
0.7%
rj175
 
0.6%
pi51
 
0.2%
mg42
 
0.1%
Other values (18)190
 
0.6%

Most occurring characters

ValueCountFrequency (%)
P19501
33.3%
E18685
31.9%
R8921
15.2%
N8711
14.9%
B812
 
1.4%
A534
 
0.9%
S440
 
0.8%
C240
 
0.4%
L217
 
0.4%
J175
 
0.3%
Other values (8)302
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter58538
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P19501
33.3%
E18685
31.9%
R8921
15.2%
N8711
14.9%
B812
 
1.4%
A534
 
0.9%
S440
 
0.8%
C240
 
0.4%
L217
 
0.4%
J175
 
0.3%
Other values (8)302
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin58538
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P19501
33.3%
E18685
31.9%
R8921
15.2%
N8711
14.9%
B812
 
1.4%
A534
 
0.9%
S440
 
0.8%
C240
 
0.4%
L217
 
0.4%
J175
 
0.3%
Other values (8)302
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII58538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P19501
33.3%
E18685
31.9%
R8921
15.2%
N8711
14.9%
B812
 
1.4%
A534
 
0.9%
S440
 
0.8%
C240
 
0.4%
L217
 
0.4%
J175
 
0.3%
Other values (8)302
 
0.5%

CD_MUNICIPIO_NASCIMENTO
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
-3
29269 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters58538
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-3
2nd row-3
3rd row-3
4th row-3
5th row-3

Common Values

ValueCountFrequency (%)
-329269
100.0%

Length

2021-07-24T20:06:36.433703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:36.502258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
329269
100.0%

Most occurring characters

ValueCountFrequency (%)
-29269
50.0%
329269
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation29269
50.0%
Decimal Number29269
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
-29269
100.0%
Decimal Number
ValueCountFrequency (%)
329269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-29269
50.0%
329269
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII58538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-29269
50.0%
329269
50.0%

NM_MUNICIPIO_NASCIMENTO
Categorical

HIGH CARDINALITY

Distinct855
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
RECIFE
2757 
NATAL
 
1568
MOSSORÓ
 
620
CARUARU
 
563
JABOATÃO DOS GUARARAPES
 
466
Other values (850)
23295 

Length

Max length26
Median length8
Mean length9.697837302
Min length3

Characters and Unicode

Total characters283846
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique284 ?
Unique (%)1.0%

Sample

1st rowRIBEIRÃO
2nd rowCABO DE SANTO AGOSTINHO
3rd rowSÃO JOSÉ DO EGITO
4th rowGRAVATÁ
5th rowSALGUEIRO

Common Values

ValueCountFrequency (%)
RECIFE2757
 
9.4%
NATAL1568
 
5.4%
MOSSORÓ620
 
2.1%
CARUARU563
 
1.9%
JABOATÃO DOS GUARARAPES466
 
1.6%
CABO DE SANTO AGOSTINHO431
 
1.5%
PAULISTA400
 
1.4%
PALMARES377
 
1.3%
GARANHUNS366
 
1.3%
LIMOEIRO341
 
1.2%
Other values (845)21380
73.0%

Length

2021-07-24T20:06:36.759730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
recife2757
 
6.0%
são1915
 
4.2%
natal1568
 
3.4%
do1520
 
3.3%
de1430
 
3.1%
santo921
 
2.0%
da827
 
1.8%
dos696
 
1.5%
mossoró620
 
1.4%
caruaru563
 
1.2%
Other values (858)32766
71.9%

Most occurring characters

ValueCountFrequency (%)
A44551
15.7%
O25319
 
8.9%
R24437
 
8.6%
E20779
 
7.3%
I19597
 
6.9%
16314
 
5.7%
S15967
 
5.6%
N14606
 
5.1%
T12370
 
4.4%
C10952
 
3.9%
Other values (39)78954
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter267259
94.2%
Space Separator16314
 
5.7%
Dash Punctuation243
 
0.1%
Lowercase Letter30
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A44551
16.7%
O25319
 
9.5%
R24437
 
9.1%
E20779
 
7.8%
I19597
 
7.3%
S15967
 
6.0%
N14606
 
5.5%
T12370
 
4.6%
C10952
 
4.1%
U9971
 
3.7%
Other values (25)68710
25.7%
Lowercase Letter
ValueCountFrequency (%)
a8
26.7%
n4
13.3%
t4
13.3%
i3
 
10.0%
o3
 
10.0%
s2
 
6.7%
m1
 
3.3%
r1
 
3.3%
d1
 
3.3%
b1
 
3.3%
Other values (2)2
 
6.7%
Space Separator
ValueCountFrequency (%)
16314
100.0%
Dash Punctuation
ValueCountFrequency (%)
-243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin267289
94.2%
Common16557
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A44551
16.7%
O25319
 
9.5%
R24437
 
9.1%
E20779
 
7.8%
I19597
 
7.3%
S15967
 
6.0%
N14606
 
5.5%
T12370
 
4.6%
C10952
 
4.1%
U9971
 
3.7%
Other values (37)68740
25.7%
Common
ValueCountFrequency (%)
16314
98.5%
-243
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII273284
96.3%
Latin 1 Sup10562
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A44551
16.3%
O25319
 
9.3%
R24437
 
8.9%
E20779
 
7.6%
I19597
 
7.2%
16314
 
6.0%
S15967
 
5.8%
N14606
 
5.3%
T12370
 
4.5%
C10952
 
4.0%
Other values (27)68392
25.0%
Latin 1 Sup
ValueCountFrequency (%)
Ã3433
32.5%
Ó1764
16.7%
É1305
 
12.4%
Á1198
 
11.3%
Ú758
 
7.2%
Ç647
 
6.1%
Í618
 
5.9%
Â468
 
4.4%
Ê198
 
1.9%
Ô160
 
1.5%
Other values (2)13
 
0.1%

DT_NASCIMENTO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct13468
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
11/03/1983
 
10
14/10/1965
 
9
05/07/1982
 
9
29/09/1981
 
9
22/02/1979
 
9
Other values (13463)
29223 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters292690
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5428 ?
Unique (%)18.5%

Sample

1st row04/05/1968
2nd row10/10/1980
3rd row01/12/1967
4th row03/09/1977
5th row26/12/1972

Common Values

ValueCountFrequency (%)
11/03/198310
 
< 0.1%
14/10/19659
 
< 0.1%
05/07/19829
 
< 0.1%
29/09/19819
 
< 0.1%
22/02/19799
 
< 0.1%
18/06/19749
 
< 0.1%
20/10/19788
 
< 0.1%
25/08/19658
 
< 0.1%
04/06/19788
 
< 0.1%
17/11/19788
 
< 0.1%
Other values (13458)29182
99.7%

Length

2021-07-24T20:06:37.043411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/03/198310
 
< 0.1%
14/10/19659
 
< 0.1%
05/07/19829
 
< 0.1%
29/09/19819
 
< 0.1%
22/02/19799
 
< 0.1%
18/06/19749
 
< 0.1%
20/10/19788
 
< 0.1%
25/08/19658
 
< 0.1%
04/06/19788
 
< 0.1%
17/11/19788
 
< 0.1%
Other values (13458)29182
99.7%

Most occurring characters

ValueCountFrequency (%)
/58538
20.0%
156803
19.4%
940189
13.7%
039743
13.6%
220293
 
6.9%
717507
 
6.0%
816504
 
5.6%
614401
 
4.9%
510490
 
3.6%
39775
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number234152
80.0%
Other Punctuation58538
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
156803
24.3%
940189
17.2%
039743
17.0%
220293
 
8.7%
717507
 
7.5%
816504
 
7.0%
614401
 
6.2%
510490
 
4.5%
39775
 
4.2%
48447
 
3.6%
Other Punctuation
ValueCountFrequency (%)
/58538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common292690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/58538
20.0%
156803
19.4%
940189
13.7%
039743
13.6%
220293
 
6.9%
717507
 
6.0%
816504
 
5.6%
614401
 
4.9%
510490
 
3.6%
39775
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII292690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/58538
20.0%
156803
19.4%
940189
13.7%
039743
13.6%
220293
 
6.9%
717507
 
6.0%
816504
 
5.6%
614401
 
4.9%
510490
 
3.6%
39775
 
3.3%

NR_IDADE_DATA_POSSE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.0925211
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:37.168323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile26
Q136
median44
Q352
95-th percentile63
Maximum93
Range77
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.3282998
Coefficient of variation (CV)0.2569211176
Kurtosis-0.2781815463
Mean44.0925211
Median Absolute Deviation (MAD)8
Skewness0.1710513793
Sum1290544
Variance128.3303763
MonotonicityNot monotonic
2021-07-24T20:06:37.323629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381006
 
3.4%
421004
 
3.4%
391000
 
3.4%
41994
 
3.4%
46969
 
3.3%
43967
 
3.3%
48954
 
3.3%
40942
 
3.2%
45929
 
3.2%
44919
 
3.1%
Other values (61)19585
66.9%
ValueCountFrequency (%)
161
 
< 0.1%
1850
 
0.2%
19107
 
0.4%
20136
0.5%
21170
0.6%
22189
0.6%
23229
0.8%
24260
0.9%
25287
1.0%
26311
1.1%
ValueCountFrequency (%)
931
 
< 0.1%
861
 
< 0.1%
851
 
< 0.1%
845
 
< 0.1%
835
 
< 0.1%
824
 
< 0.1%
818
< 0.1%
807
< 0.1%
795
 
< 0.1%
7816
0.1%

NR_TITULO_ELEITORAL_CANDIDATO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29248
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.195649374 × 1010
Minimum1861643
Maximum4.466430901 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:37.540159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1861643
5-th percentile5925635638
Q11.809688164 × 1010
median3.54803016 × 1010
Q35.737670081 × 1010
95-th percentile8.811822084 × 1010
Maximum4.466430901 × 1011
Range4.466412285 × 1011
Interquartile range (IQR)3.927981917 × 1010

Descriptive statistics

Standard deviation3.586608738 × 1010
Coefficient of variation (CV)0.8548399589
Kurtosis24.44809591
Mean4.195649374 × 1010
Median Absolute Deviation (MAD)1.896359997 × 1010
Skewness3.677645537
Sum1.228024615 × 1015
Variance1.286376224 × 1021
MonotonicityNot monotonic
2021-07-24T20:06:37.686086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.747118081 × 10102
 
< 0.1%
2.424274169 × 10102
 
< 0.1%
1.111430164 × 10102
 
< 0.1%
1.370942089 × 10102
 
< 0.1%
5.847753085 × 10102
 
< 0.1%
1.239902168 × 10102
 
< 0.1%
6.284123088 × 10102
 
< 0.1%
4.166361087 × 10102
 
< 0.1%
6.193951081 × 10102
 
< 0.1%
5.838461088 × 10102
 
< 0.1%
Other values (29238)29249
99.9%
ValueCountFrequency (%)
18616431
< 0.1%
44916941
< 0.1%
82708331
< 0.1%
98316001
< 0.1%
115916271
< 0.1%
207316781
< 0.1%
226108681
< 0.1%
387616861
< 0.1%
399408251
< 0.1%
477508921
< 0.1%
ValueCountFrequency (%)
4.466430901 × 10111
< 0.1%
4.444921501 × 10111
< 0.1%
4.316098802 × 10111
< 0.1%
4.222241501 × 10111
< 0.1%
4.195454602 × 10111
< 0.1%
4.079063601 × 10111
< 0.1%
4.073169002 × 10111
< 0.1%
4.067374101 × 10111
< 0.1%
4.062011802 × 10111
< 0.1%
4.018980702 × 10111
< 0.1%

CD_GENERO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
2
19311 
4
9958 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row2
4th row2
5th row4

Common Values

ValueCountFrequency (%)
219311
66.0%
49958
34.0%

Length

2021-07-24T20:06:37.918154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:37.990332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
219311
66.0%
49958
34.0%

Most occurring characters

ValueCountFrequency (%)
219311
66.0%
49958
34.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
219311
66.0%
49958
34.0%

Most occurring scripts

ValueCountFrequency (%)
Common29269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
219311
66.0%
49958
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
219311
66.0%
49958
34.0%

DS_GENERO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
MASCULINO
19311 
FEMININO
9958 

Length

Max length9
Median length9
Mean length8.659776555
Min length8

Characters and Unicode

Total characters253463
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEMININO
2nd rowMASCULINO
3rd rowMASCULINO
4th rowMASCULINO
5th rowFEMININO

Common Values

ValueCountFrequency (%)
MASCULINO19311
66.0%
FEMININO9958
34.0%

Length

2021-07-24T20:06:38.190554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:38.265716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
masculino19311
66.0%
feminino9958
34.0%

Most occurring characters

ValueCountFrequency (%)
I39227
15.5%
N39227
15.5%
M29269
11.5%
O29269
11.5%
A19311
7.6%
S19311
7.6%
C19311
7.6%
U19311
7.6%
L19311
7.6%
F9958
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter253463
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I39227
15.5%
N39227
15.5%
M29269
11.5%
O29269
11.5%
A19311
7.6%
S19311
7.6%
C19311
7.6%
U19311
7.6%
L19311
7.6%
F9958
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Latin253463
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I39227
15.5%
N39227
15.5%
M29269
11.5%
O29269
11.5%
A19311
7.6%
S19311
7.6%
C19311
7.6%
U19311
7.6%
L19311
7.6%
F9958
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII253463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I39227
15.5%
N39227
15.5%
M29269
11.5%
O29269
11.5%
A19311
7.6%
S19311
7.6%
C19311
7.6%
U19311
7.6%
L19311
7.6%
F9958
 
3.9%

CD_GRAU_INSTRUCAO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.627387338
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:38.333294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.701544514
Coefficient of variation (CV)0.3023684727
Kurtosis-0.7435443632
Mean5.627387338
Median Absolute Deviation (MAD)1
Skewness-0.339960152
Sum164708
Variance2.895253735
MonotonicityNot monotonic
2021-07-24T20:06:38.429682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
612667
43.3%
85731
19.6%
34027
 
13.8%
43184
 
10.9%
51584
 
5.4%
71194
 
4.1%
2881
 
3.0%
11
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
2881
 
3.0%
34027
 
13.8%
43184
 
10.9%
51584
 
5.4%
612667
43.3%
71194
 
4.1%
85731
19.6%
ValueCountFrequency (%)
85731
19.6%
71194
 
4.1%
612667
43.3%
51584
 
5.4%
43184
 
10.9%
34027
 
13.8%
2881
 
3.0%
11
 
< 0.1%

DS_GRAU_INSTRUCAO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
ENSINO MÉDIO COMPLETO
12667 
SUPERIOR COMPLETO
5731 
ENSINO FUNDAMENTAL INCOMPLETO
4027 
ENSINO FUNDAMENTAL COMPLETO
3184 
ENSINO MÉDIO INCOMPLETO
1584 
Other values (3)
2076 

Length

Max length29
Median length21
Mean length21.7255458
Min length10

Characters and Unicode

Total characters635885
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLÊ E ESCREVE
2nd rowENSINO MÉDIO INCOMPLETO
3rd rowENSINO FUNDAMENTAL COMPLETO
4th rowENSINO FUNDAMENTAL INCOMPLETO
5th rowENSINO MÉDIO COMPLETO

Common Values

ValueCountFrequency (%)
ENSINO MÉDIO COMPLETO12667
43.3%
SUPERIOR COMPLETO5731
19.6%
ENSINO FUNDAMENTAL INCOMPLETO4027
 
13.8%
ENSINO FUNDAMENTAL COMPLETO3184
 
10.9%
ENSINO MÉDIO INCOMPLETO1584
 
5.4%
SUPERIOR INCOMPLETO1194
 
4.1%
LÊ E ESCREVE881
 
3.0%
ANALFABETO1
 
< 0.1%

Length

2021-07-24T20:06:38.717215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:38.815072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
completo21582
26.7%
ensino21462
26.5%
médio14251
17.6%
fundamental7211
 
8.9%
superior6925
 
8.6%
incompleto6805
 
8.4%
881
 
1.1%
e881
 
1.1%
escreve881
 
1.1%
analfabeto1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O99413
15.6%
E67510
10.6%
N64152
10.1%
51611
8.1%
M49849
7.8%
I49443
7.8%
L36480
 
5.7%
T35599
 
5.6%
P35312
 
5.6%
S29268
 
4.6%
Other values (10)117248
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter584274
91.9%
Space Separator51611
 
8.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O99413
17.0%
E67510
11.6%
N64152
11.0%
M49849
8.5%
I49443
8.5%
L36480
 
6.2%
T35599
 
6.1%
P35312
 
6.0%
S29268
 
5.0%
C29268
 
5.0%
Other values (9)87980
15.1%
Space Separator
ValueCountFrequency (%)
51611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin584274
91.9%
Common51611
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O99413
17.0%
E67510
11.6%
N64152
11.0%
M49849
8.5%
I49443
8.5%
L36480
 
6.2%
T35599
 
6.1%
P35312
 
6.0%
S29268
 
5.0%
C29268
 
5.0%
Other values (9)87980
15.1%
Common
ValueCountFrequency (%)
51611
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII620753
97.6%
Latin 1 Sup15132
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O99413
16.0%
E67510
10.9%
N64152
10.3%
51611
8.3%
M49849
8.0%
I49443
8.0%
L36480
 
5.9%
T35599
 
5.7%
P35312
 
5.7%
S29268
 
4.7%
Other values (8)102116
16.5%
Latin 1 Sup
ValueCountFrequency (%)
É14251
94.2%
Ê881
 
5.8%

CD_ESTADO_CIVIL
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
3
14592 
1
11998 
9
2095 
5
 
460
7
 
124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

Length

2021-07-24T20:06:39.067835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:39.151587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

Most occurring characters

ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common29269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
314592
49.9%
111998
41.0%
92095
 
7.2%
5460
 
1.6%
7124
 
0.4%

DS_ESTADO_CIVIL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
CASADO(A)
14592 
SOLTEIRO(A)
11998 
DIVORCIADO(A)
2095 
VIÚVO(A)
 
460
SEPARADO(A) JUDICIALMENTE
 
124

Length

Max length25
Median length9
Mean length10.15822201
Min length8

Characters and Unicode

Total characters297321
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCASADO(A)
2nd rowSOLTEIRO(A)
3rd rowCASADO(A)
4th rowCASADO(A)
5th rowSOLTEIRO(A)

Common Values

ValueCountFrequency (%)
CASADO(A)14592
49.9%
SOLTEIRO(A)11998
41.0%
DIVORCIADO(A)2095
 
7.2%
VIÚVO(A)460
 
1.6%
SEPARADO(A) JUDICIALMENTE124
 
0.4%

Length

2021-07-24T20:06:39.374351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:39.460574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
casado(a14592
49.6%
solteiro(a11998
40.8%
divorciado(a2095
 
7.1%
viúvo(a460
 
1.6%
separado(a124
 
0.4%
judicialmente124
 
0.4%

Most occurring characters

ValueCountFrequency (%)
A60920
20.5%
O43362
14.6%
(29269
9.8%
)29269
9.8%
S26714
9.0%
D19030
 
6.4%
I16896
 
5.7%
C16811
 
5.7%
R14217
 
4.8%
E12370
 
4.2%
Other values (10)28463
9.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter238659
80.3%
Open Punctuation29269
 
9.8%
Close Punctuation29269
 
9.8%
Space Separator124
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A60920
25.5%
O43362
18.2%
S26714
11.2%
D19030
 
8.0%
I16896
 
7.1%
C16811
 
7.0%
R14217
 
6.0%
E12370
 
5.2%
L12122
 
5.1%
T12122
 
5.1%
Other values (7)4095
 
1.7%
Open Punctuation
ValueCountFrequency (%)
(29269
100.0%
Close Punctuation
ValueCountFrequency (%)
)29269
100.0%
Space Separator
ValueCountFrequency (%)
124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238659
80.3%
Common58662
 
19.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A60920
25.5%
O43362
18.2%
S26714
11.2%
D19030
 
8.0%
I16896
 
7.1%
C16811
 
7.0%
R14217
 
6.0%
E12370
 
5.2%
L12122
 
5.1%
T12122
 
5.1%
Other values (7)4095
 
1.7%
Common
ValueCountFrequency (%)
(29269
49.9%
)29269
49.9%
124
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII296861
99.8%
Latin 1 Sup460
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A60920
20.5%
O43362
14.6%
(29269
9.9%
)29269
9.9%
S26714
9.0%
D19030
 
6.4%
I16896
 
5.7%
C16811
 
5.7%
R14217
 
4.8%
E12370
 
4.2%
Other values (9)28003
9.4%
Latin 1 Sup
ValueCountFrequency (%)
Ú460
100.0%

CD_COR_RACA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.204243397
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:39.562133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.007946982
Coefficient of variation (CV)0.4572757181
Kurtosis-0.172682273
Mean2.204243397
Median Absolute Deviation (MAD)0
Skewness0.1692089897
Sum64516
Variance1.015957119
MonotonicityNot monotonic
2021-07-24T20:06:39.680241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
315106
51.6%
110620
36.3%
23094
 
10.6%
6232
 
0.8%
5130
 
0.4%
487
 
0.3%
ValueCountFrequency (%)
110620
36.3%
23094
 
10.6%
315106
51.6%
487
 
0.3%
5130
 
0.4%
6232
 
0.8%
ValueCountFrequency (%)
6232
 
0.8%
5130
 
0.4%
487
 
0.3%
315106
51.6%
23094
 
10.6%
110620
36.3%

DS_COR_RACA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
PARDA
15106 
BRANCA
10620 
PRETA
3094 
SEM INFORMAÇÃO
 
232
INDÍGENA
 
130

Length

Max length14
Median length5
Mean length5.453449042
Min length5

Characters and Unicode

Total characters159617
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRANCA
2nd rowPARDA
3rd rowPARDA
4th rowBRANCA
5th rowPARDA

Common Values

ValueCountFrequency (%)
PARDA15106
51.6%
BRANCA10620
36.3%
PRETA3094
 
10.6%
SEM INFORMAÇÃO232
 
0.8%
INDÍGENA130
 
0.4%
AMARELA87
 
0.3%

Length

2021-07-24T20:06:39.957047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:40.045546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
parda15106
51.2%
branca10620
36.0%
preta3094
 
10.5%
sem232
 
0.8%
informação232
 
0.8%
indígena130
 
0.4%
amarela87
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A55169
34.6%
R29139
18.3%
P18200
 
11.4%
D15236
 
9.5%
N11112
 
7.0%
B10620
 
6.7%
C10620
 
6.7%
E3543
 
2.2%
T3094
 
1.9%
M551
 
0.3%
Other values (10)2333
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter159385
99.9%
Space Separator232
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A55169
34.6%
R29139
18.3%
P18200
 
11.4%
D15236
 
9.6%
N11112
 
7.0%
B10620
 
6.7%
C10620
 
6.7%
E3543
 
2.2%
T3094
 
1.9%
M551
 
0.3%
Other values (9)2101
 
1.3%
Space Separator
ValueCountFrequency (%)
232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin159385
99.9%
Common232
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A55169
34.6%
R29139
18.3%
P18200
 
11.4%
D15236
 
9.6%
N11112
 
7.0%
B10620
 
6.7%
C10620
 
6.7%
E3543
 
2.2%
T3094
 
1.9%
M551
 
0.3%
Other values (9)2101
 
1.3%
Common
ValueCountFrequency (%)
232
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII159023
99.6%
Latin 1 Sup594
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A55169
34.7%
R29139
18.3%
P18200
 
11.4%
D15236
 
9.6%
N11112
 
7.0%
B10620
 
6.7%
C10620
 
6.7%
E3543
 
2.2%
T3094
 
1.9%
M551
 
0.3%
Other values (7)1739
 
1.1%
Latin 1 Sup
ValueCountFrequency (%)
Ç232
39.1%
Ã232
39.1%
Í130
21.9%

CD_OCUPACAO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct198
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean532.3448017
Minimum101
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:40.189830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile131
Q1257
median532
Q3931
95-th percentile999
Maximum999
Range898
Interquartile range (IQR)674

Descriptive statistics

Standard deviation321.9718176
Coefficient of variation (CV)0.6048181866
Kurtosis-1.39152458
Mean532.3448017
Median Absolute Deviation (MAD)278
Skewness0.3528004752
Sum15581200
Variance103665.8513
MonotonicityNot monotonic
2021-07-24T20:06:40.343863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9996756
23.1%
6013980
13.6%
1691945
 
6.6%
2781844
 
6.3%
2981490
 
5.1%
2571073
 
3.7%
581992
 
3.4%
931678
 
2.3%
923618
 
2.1%
265608
 
2.1%
Other values (188)9285
31.7%
ValueCountFrequency (%)
10192
 
0.3%
10212
 
< 0.1%
10311
 
< 0.1%
1041
 
< 0.1%
1061
 
< 0.1%
109326
1.1%
11014
 
< 0.1%
11159
 
0.2%
11222
 
0.1%
113229
0.8%
ValueCountFrequency (%)
9996756
23.1%
931678
 
2.3%
923618
 
2.1%
92257
 
0.2%
92165
 
0.2%
91026
 
0.1%
7151
 
< 0.1%
71359
 
0.2%
7124
 
< 0.1%
7112
 
< 0.1%

DS_OCUPACAO
Categorical

HIGH CARDINALITY

Distinct198
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
OUTROS
6756 
AGRICULTOR
3980 
COMERCIANTE
1945 
VEREADOR
1844 
SERVIDOR PÚBLICO MUNICIPAL
1490 
Other values (193)
13254 

Length

Max length70
Median length10
Mean length16.3958796
Min length6

Characters and Unicode

Total characters479891
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowADMINISTRADOR
2nd rowOUTROS
3rd rowAGRICULTOR
4th rowOUTROS
5th rowOUTROS

Common Values

ValueCountFrequency (%)
OUTROS6756
23.1%
AGRICULTOR3980
13.6%
COMERCIANTE1945
 
6.6%
VEREADOR1844
 
6.3%
SERVIDOR PÚBLICO MUNICIPAL1490
 
5.1%
EMPRESÁRIO1073
 
3.7%
DONA DE CASA992
 
3.4%
ESTUDANTE, BOLSISTA, ESTAGIÁRIO E ASSEMELHADOS678
 
2.3%
APOSENTADO (EXCETO SERVIDOR PÚBLICO)618
 
2.1%
PROFESSOR DE ENSINO FUNDAMENTAL608
 
2.1%
Other values (188)9285
31.7%

Length

2021-07-24T20:06:40.697876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
outros6756
 
11.1%
de5587
 
9.2%
agricultor3980
 
6.5%
e3705
 
6.1%
servidor2553
 
4.2%
público2553
 
4.2%
comerciante1945
 
3.2%
vereador1844
 
3.0%
assemelhados1782
 
2.9%
municipal1490
 
2.4%
Other values (325)28774
47.2%

Most occurring characters

ValueCountFrequency (%)
O54407
11.3%
E48696
10.1%
R46929
9.8%
A39591
 
8.2%
I35145
 
7.3%
S33392
 
7.0%
31700
 
6.6%
T31170
 
6.5%
C21885
 
4.6%
D20932
 
4.4%
Other values (30)116044
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter443589
92.4%
Space Separator31700
 
6.6%
Other Punctuation2550
 
0.5%
Open Punctuation928
 
0.2%
Close Punctuation928
 
0.2%
Dash Punctuation196
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O54407
12.3%
E48696
11.0%
R46929
10.6%
A39591
8.9%
I35145
 
7.9%
S33392
 
7.5%
T31170
 
7.0%
C21885
 
4.9%
D20932
 
4.7%
L16897
 
3.8%
Other values (25)94545
21.3%
Space Separator
ValueCountFrequency (%)
31700
100.0%
Other Punctuation
ValueCountFrequency (%)
,2550
100.0%
Open Punctuation
ValueCountFrequency (%)
(928
100.0%
Close Punctuation
ValueCountFrequency (%)
)928
100.0%
Dash Punctuation
ValueCountFrequency (%)
-196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin443589
92.4%
Common36302
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O54407
12.3%
E48696
11.0%
R46929
10.6%
A39591
8.9%
I35145
 
7.9%
S33392
 
7.5%
T31170
 
7.0%
C21885
 
4.9%
D20932
 
4.7%
L16897
 
3.8%
Other values (25)94545
21.3%
Common
ValueCountFrequency (%)
31700
87.3%
,2550
 
7.0%
(928
 
2.6%
)928
 
2.6%
-196
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII470637
98.1%
Latin 1 Sup9254
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O54407
11.6%
E48696
10.3%
R46929
10.0%
A39591
 
8.4%
I35145
 
7.5%
S33392
 
7.1%
31700
 
6.7%
T31170
 
6.6%
C21885
 
4.7%
D20932
 
4.4%
Other values (19)106790
22.7%
Latin 1 Sup
ValueCountFrequency (%)
Ú3027
32.7%
Á2229
24.1%
É1403
15.2%
Í639
 
6.9%
Ã617
 
6.7%
Ó568
 
6.1%
Ç518
 
5.6%
Â133
 
1.4%
Ô52
 
0.6%
Õ47
 
0.5%

VR_DESPESA_MAX_CAMPANHA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct163
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80446.86454
Minimum12307.75
Maximum1011149.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:40.881960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12307.75
5-th percentile12307.75
Q112307.75
median24617.75
Q352266.32
95-th percentile386587.77
Maximum1011149.65
Range998841.9
Interquartile range (IQR)39958.57

Descriptive statistics

Standard deviation179853.6182
Coefficient of variation (CV)2.235682139
Kurtosis19.18798663
Mean80446.86454
Median Absolute Deviation (MAD)12310
Skewness4.352770347
Sum2354599278
Variance3.234732398 × 1010
MonotonicityNot monotonic
2021-07-24T20:06:41.047794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12307.758912
30.4%
1011149.65896
 
3.1%
138639.74757
 
2.6%
386587.77736
 
2.5%
105385.98584
 
2.0%
109902.99561
 
1.9%
55870.31553
 
1.9%
222300.63476
 
1.6%
149376.8468
 
1.6%
24175.84468
 
1.6%
Other values (153)14858
50.8%
ValueCountFrequency (%)
12307.758912
30.4%
15268154
 
0.5%
15281.3520
 
0.1%
15449.0337
 
0.1%
15466.5772
 
0.2%
15585.5949
 
0.2%
15674.5716
 
0.1%
15733.2332
 
0.1%
16224.9723
 
0.1%
16330.7629
 
0.1%
ValueCountFrequency (%)
1011149.65896
3.1%
386587.77736
2.5%
222300.63476
1.6%
210013.89339
 
1.2%
149376.8468
1.6%
138639.74757
2.6%
134612.46273
 
0.9%
131218.52373
1.3%
109902.99561
1.9%
106601.98307
 
1.0%

CD_SIT_TOT_TURNO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
5
17080 
4
7487 
2
2861 
3
 
944
-1
 
897

Length

Max length2
Median length1
Mean length1.030646759
Min length1

Characters and Unicode

Total characters30166
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row4
5th row5

Common Values

ValueCountFrequency (%)
517080
58.4%
47487
25.6%
22861
 
9.8%
3944
 
3.2%
-1897
 
3.1%

Length

2021-07-24T20:06:41.300029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:41.385561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
517080
58.4%
47487
25.6%
22861
 
9.8%
3944
 
3.2%
1897
 
3.1%

Most occurring characters

ValueCountFrequency (%)
517080
56.6%
47487
24.8%
22861
 
9.5%
3944
 
3.1%
-897
 
3.0%
1897
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29269
97.0%
Dash Punctuation897
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
517080
58.4%
47487
25.6%
22861
 
9.8%
3944
 
3.2%
1897
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30166
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
517080
56.6%
47487
24.8%
22861
 
9.5%
3944
 
3.1%
-897
 
3.0%
1897
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
517080
56.6%
47487
24.8%
22861
 
9.5%
3944
 
3.1%
-897
 
3.0%
1897
 
3.0%

DS_SIT_TOT_TURNO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
SUPLENTE
17080 
NÃO ELEITO
7487 
ELEITO POR QP
2861 
ELEITO POR MÉDIA
 
944
#NULO#
 
897

Length

Max length16
Median length8
Mean length9.197068571
Min length6

Characters and Unicode

Total characters269189
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUPLENTE
2nd rowSUPLENTE
3rd rowSUPLENTE
4th rowNÃO ELEITO
5th rowSUPLENTE

Common Values

ValueCountFrequency (%)
SUPLENTE17080
58.4%
NÃO ELEITO7487
25.6%
ELEITO POR QP2861
 
9.8%
ELEITO POR MÉDIA944
 
3.2%
#NULO#897
 
3.1%

Length

2021-07-24T20:06:41.636894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:41.720846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
suplente17080
38.5%
eleito11292
25.5%
não7487
16.9%
por3805
 
8.6%
qp2861
 
6.4%
média944
 
2.1%
nulo897
 
2.0%

Most occurring characters

ValueCountFrequency (%)
E56744
21.1%
L29269
10.9%
T28372
10.5%
N25464
9.5%
P23746
8.8%
O23481
8.7%
U17977
 
6.7%
S17080
 
6.3%
15097
 
5.6%
I12236
 
4.5%
Other values (8)19723
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter252298
93.7%
Space Separator15097
 
5.6%
Other Punctuation1794
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E56744
22.5%
L29269
11.6%
T28372
11.2%
N25464
10.1%
P23746
9.4%
O23481
9.3%
U17977
 
7.1%
S17080
 
6.8%
I12236
 
4.8%
Ã7487
 
3.0%
Other values (6)10442
 
4.1%
Space Separator
ValueCountFrequency (%)
15097
100.0%
Other Punctuation
ValueCountFrequency (%)
#1794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin252298
93.7%
Common16891
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E56744
22.5%
L29269
11.6%
T28372
11.2%
N25464
10.1%
P23746
9.4%
O23481
9.3%
U17977
 
7.1%
S17080
 
6.8%
I12236
 
4.8%
Ã7487
 
3.0%
Other values (6)10442
 
4.1%
Common
ValueCountFrequency (%)
15097
89.4%
#1794
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII260758
96.9%
Latin 1 Sup8431
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E56744
21.8%
L29269
11.2%
T28372
10.9%
N25464
9.8%
P23746
9.1%
O23481
9.0%
U17977
 
6.9%
S17080
 
6.6%
15097
 
5.8%
I12236
 
4.7%
Other values (6)11292
 
4.3%
Latin 1 Sup
ValueCountFrequency (%)
Ã7487
88.8%
É944
 
11.2%

ST_REELEICAO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
N
27885 
S
 
1384

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N27885
95.3%
S1384
 
4.7%

Length

2021-07-24T20:06:41.939618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:42.020455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
n27885
95.3%
s1384
 
4.7%

Most occurring characters

ValueCountFrequency (%)
N27885
95.3%
S1384
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter29269
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N27885
95.3%
S1384
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin29269
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N27885
95.3%
S1384
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N27885
95.3%
S1384
 
4.7%

ST_DECLARAR_BENS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
S
24283 
N
4986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29269
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowN
3rd rowS
4th rowN
5th rowS

Common Values

ValueCountFrequency (%)
S24283
83.0%
N4986
 
17.0%

Length

2021-07-24T20:06:42.214851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:42.286777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
s24283
83.0%
n4986
 
17.0%

Most occurring characters

ValueCountFrequency (%)
S24283
83.0%
N4986
 
17.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter29269
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S24283
83.0%
N4986
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
Latin29269
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S24283
83.0%
N4986
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S24283
83.0%
N4986
 
17.0%

NR_PROTOCOLO_CANDIDATURA
Categorical

CONSTANT
HIGH CORRELATION
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
-1
29269 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters58538
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-129269
100.0%

Length

2021-07-24T20:06:42.472792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:42.542461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
129269
100.0%

Most occurring characters

ValueCountFrequency (%)
-29269
50.0%
129269
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation29269
50.0%
Decimal Number29269
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
-29269
100.0%
Decimal Number
ValueCountFrequency (%)
129269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common58538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-29269
50.0%
129269
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII58538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-29269
50.0%
129269
50.0%

NR_PROCESSO
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct29269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.00223464 × 1018
Minimum6.00012572 × 1018
Maximum6.01168752 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.8 KiB
2021-07-24T20:06:42.627247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6.00012572 × 1018
5-th percentile6.00060026 × 1018
Q16.00121692 × 1018
median6.00191312 × 1018
Q36.00287582 × 1018
95-th percentile6.00516638 × 1018
Maximum6.01168752 × 1018
Range1.15618 × 1016
Interquartile range (IQR)1.6589 × 1015

Descriptive statistics

Standard deviation1.400676096 × 1015
Coefficient of variation (CV)0.0002333591037
Kurtosis2.013416717
Mean6.00223464 × 1018
Median Absolute Deviation (MAD)7.884 × 1014
Skewness1.315735008
Sum-7.384873995 × 1018
Variance1.961893525 × 1030
MonotonicityNot monotonic
2021-07-24T20:06:42.784930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.00288312 × 10181
 
< 0.1%
6.00178112 × 10181
 
< 0.1%
6.00566802 × 10181
 
< 0.1%
6.00369682 × 10181
 
< 0.1%
6.00053212 × 10181
 
< 0.1%
6.00241812 × 10181
 
< 0.1%
6.00141732 × 10181
 
< 0.1%
6.00162242 × 10181
 
< 0.1%
6.00443942 × 10181
 
< 0.1%
6.00196622 × 10181
 
< 0.1%
Other values (29259)29259
> 99.9%
ValueCountFrequency (%)
6.00012572 × 10181
< 0.1%
6.00013422 × 10181
< 0.1%
6.00013972 × 10181
< 0.1%
6.00014272 × 10181
< 0.1%
6.00014822 × 10181
< 0.1%
6.00015112 × 10181
< 0.1%
6.00015122 × 10181
< 0.1%
6.00016932 × 10181
< 0.1%
6.00016942 × 10181
< 0.1%
6.00017782 × 10181
< 0.1%
ValueCountFrequency (%)
6.01168752 × 10181
< 0.1%
6.01155222 × 10181
< 0.1%
6.01154372 × 10181
< 0.1%
6.01135392 × 10181
< 0.1%
6.01033782 × 10181
< 0.1%
6.01031112 × 10181
< 0.1%
6.01029412 × 10181
< 0.1%
6.01028562 × 10181
< 0.1%
6.00948652 × 10181
< 0.1%
6.00875842 × 10181
< 0.1%

CD_SITUACAO_CANDIDATO_PLEITO
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.063275138
Minimum-1
Maximum17
Zeros0
Zeros (%)0.0%
Negative897
Negative (%)3.1%
Memory size228.8 KiB
2021-07-24T20:06:42.911276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum17
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.379490313
Coefficient of variation (CV)0.6685925148
Kurtosis60.56552857
Mean2.063275138
Median Absolute Deviation (MAD)0
Skewness6.843158068
Sum60390
Variance1.902993523
MonotonicityNot monotonic
2021-07-24T20:06:43.008173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
227765
94.9%
-1897
 
3.1%
14276
 
0.9%
4183
 
0.6%
6117
 
0.4%
1622
 
0.1%
133
 
< 0.1%
173
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
-1897
 
3.1%
227765
94.9%
4183
 
0.6%
52
 
< 0.1%
6117
 
0.4%
71
 
< 0.1%
133
 
< 0.1%
14276
 
0.9%
1622
 
0.1%
173
 
< 0.1%
ValueCountFrequency (%)
173
 
< 0.1%
1622
 
0.1%
14276
 
0.9%
133
 
< 0.1%
71
 
< 0.1%
6117
 
0.4%
52
 
< 0.1%
4183
 
0.6%
227765
94.9%
-1897
 
3.1%

DS_SITUACAO_CANDIDATO_PLEITO
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
DEFERIDO
27765 
#NULO#
 
897
INDEFERIDO
 
276
INDEFERIDO COM RECURSO
 
183
RENÚNCIA
 
117
Other values (5)
 
31

Length

Max length22
Median length8
Mean length8.056851959
Min length6

Characters and Unicode

Total characters235816
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO27765
94.9%
#NULO#897
 
3.1%
INDEFERIDO276
 
0.9%
INDEFERIDO COM RECURSO183
 
0.6%
RENÚNCIA117
 
0.4%
DEFERIDO COM RECURSO22
 
0.1%
PEDIDO NÃO CONHECIDO3
 
< 0.1%
PENDENTE DE JULGAMENTO3
 
< 0.1%
CANCELADO2
 
< 0.1%
FALECIDO1
 
< 0.1%

Length

2021-07-24T20:06:43.235761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:43.341333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
deferido27787
93.6%
nulo897
 
3.0%
indeferido459
 
1.5%
com205
 
0.7%
recurso205
 
0.7%
renúncia117
 
0.4%
pedido3
 
< 0.1%
não3
 
< 0.1%
conhecido3
 
< 0.1%
pendente3
 
< 0.1%
Other values (4)9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E56838
24.1%
D56510
24.0%
O29571
12.5%
I28829
12.2%
R28773
12.2%
F28247
12.0%
#1794
 
0.8%
N1607
 
0.7%
U1105
 
0.5%
L903
 
0.4%
Other values (12)1639
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter233600
99.1%
Other Punctuation1794
 
0.8%
Space Separator422
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E56838
24.3%
D56510
24.2%
O29571
12.7%
I28829
12.3%
R28773
12.3%
F28247
12.1%
N1607
 
0.7%
U1105
 
0.5%
L903
 
0.4%
C538
 
0.2%
Other values (10)679
 
0.3%
Other Punctuation
ValueCountFrequency (%)
#1794
100.0%
Space Separator
ValueCountFrequency (%)
422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin233600
99.1%
Common2216
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E56838
24.3%
D56510
24.2%
O29571
12.7%
I28829
12.3%
R28773
12.3%
F28247
12.1%
N1607
 
0.7%
U1105
 
0.5%
L903
 
0.4%
C538
 
0.2%
Other values (10)679
 
0.3%
Common
ValueCountFrequency (%)
#1794
81.0%
422
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII235696
99.9%
Latin 1 Sup120
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E56838
24.1%
D56510
24.0%
O29571
12.5%
I28829
12.2%
R28773
12.2%
F28247
12.0%
#1794
 
0.8%
N1607
 
0.7%
U1105
 
0.5%
L903
 
0.4%
Other values (10)1519
 
0.6%
Latin 1 Sup
ValueCountFrequency (%)
Ú117
97.5%
Ã3
 
2.5%

CD_SITUACAO_CANDIDATO_URNA
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.054665346
Minimum-1
Maximum20
Zeros0
Zeros (%)0.0%
Negative897
Negative (%)3.1%
Memory size228.8 KiB
2021-07-24T20:06:43.475049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum20
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.379635457
Coefficient of variation (CV)0.671464801
Kurtosis89.11920612
Mean2.054665346
Median Absolute Deviation (MAD)0
Skewness8.299841542
Sum60138
Variance1.903393995
MonotonicityNot monotonic
2021-07-24T20:06:43.561228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
227560
94.2%
-1897
 
3.1%
4601
 
2.1%
17117
 
0.4%
1689
 
0.3%
203
 
< 0.1%
192
 
< 0.1%
ValueCountFrequency (%)
-1897
 
3.1%
227560
94.2%
4601
 
2.1%
1689
 
0.3%
17117
 
0.4%
192
 
< 0.1%
203
 
< 0.1%
ValueCountFrequency (%)
203
 
< 0.1%
192
 
< 0.1%
17117
 
0.4%
1689
 
0.3%
4601
 
2.1%
227560
94.2%
-1897
 
3.1%

DS_SITUACAO_CANDIDATO_URNA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
DEFERIDO
27560 
#NULO#
 
897
INDEFERIDO COM RECURSO
 
601
PENDENTE DE JULGAMENTO
 
117
DEFERIDO COM RECURSO
 
89
Other values (2)
 
5

Length

Max length32
Median length8
Mean length8.321978886
Min length6

Characters and Unicode

Total characters243576
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEFERIDO
2nd rowDEFERIDO
3rd rowDEFERIDO
4th rowDEFERIDO
5th rowDEFERIDO

Common Values

ValueCountFrequency (%)
DEFERIDO27560
94.2%
#NULO#897
 
3.1%
INDEFERIDO COM RECURSO601
 
2.1%
PENDENTE DE JULGAMENTO117
 
0.4%
DEFERIDO COM RECURSO89
 
0.3%
PEDIDO NÃO CONHECIDO COM RECURSO3
 
< 0.1%
CANCELADO COM RECURSO2
 
< 0.1%

Length

2021-07-24T20:06:43.776392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:43.873888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
deferido27649
89.5%
nulo897
 
2.9%
com695
 
2.2%
recurso695
 
2.2%
indeferido601
 
1.9%
pendente117
 
0.4%
de117
 
0.4%
julgamento117
 
0.4%
pedido3
 
< 0.1%
não3
 
< 0.1%
Other values (2)5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E57788
23.7%
D56745
23.3%
O30668
12.6%
R29640
12.2%
I28857
11.8%
F28250
11.6%
N1857
 
0.8%
#1794
 
0.7%
U1709
 
0.7%
1630
 
0.7%
Other values (11)4638
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter240152
98.6%
Other Punctuation1794
 
0.7%
Space Separator1630
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E57788
24.1%
D56745
23.6%
O30668
12.8%
R29640
12.3%
I28857
12.0%
F28250
11.8%
N1857
 
0.8%
U1709
 
0.7%
C1400
 
0.6%
L1016
 
0.4%
Other values (9)2222
 
0.9%
Other Punctuation
ValueCountFrequency (%)
#1794
100.0%
Space Separator
ValueCountFrequency (%)
1630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin240152
98.6%
Common3424
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E57788
24.1%
D56745
23.6%
O30668
12.8%
R29640
12.3%
I28857
12.0%
F28250
11.8%
N1857
 
0.8%
U1709
 
0.7%
C1400
 
0.6%
L1016
 
0.4%
Other values (9)2222
 
0.9%
Common
ValueCountFrequency (%)
#1794
52.4%
1630
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII243573
> 99.9%
Latin 1 Sup3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E57788
23.7%
D56745
23.3%
O30668
12.6%
R29640
12.2%
I28857
11.8%
F28250
11.6%
N1857
 
0.8%
#1794
 
0.7%
U1709
 
0.7%
1630
 
0.7%
Other values (10)4635
 
1.9%
Latin 1 Sup
ValueCountFrequency (%)
Ã3
100.0%

ST_CANDIDATO_INSERIDO_URNA
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.8 KiB
SIM
28373 
NÃO
 
896

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters87807
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSIM
2nd rowSIM
3rd rowSIM
4th rowSIM
5th rowSIM

Common Values

ValueCountFrequency (%)
SIM28373
96.9%
NÃO896
 
3.1%

Length

2021-07-24T20:06:44.735697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-24T20:06:44.786022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sim28373
96.9%
não896
 
3.1%

Most occurring characters

ValueCountFrequency (%)
S28373
32.3%
I28373
32.3%
M28373
32.3%
N896
 
1.0%
Ã896
 
1.0%
O896
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter87807
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S28373
32.3%
I28373
32.3%
M28373
32.3%
N896
 
1.0%
Ã896
 
1.0%
O896
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin87807
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S28373
32.3%
I28373
32.3%
M28373
32.3%
N896
 
1.0%
Ã896
 
1.0%
O896
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII86911
99.0%
Latin 1 Sup896
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S28373
32.6%
I28373
32.6%
M28373
32.6%
N896
 
1.0%
O896
 
1.0%
Latin 1 Sup
ValueCountFrequency (%)
Ã896
100.0%

Interactions

2021-07-24T20:05:50.274402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.385333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.485288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.584235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.697259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.801602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:50.903582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.007876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.107513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.217910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.321895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.424790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.520663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.738798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.860132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:51.959849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.055330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.147666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.256603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.361619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.463442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.572514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.674840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.783548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.888931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:52.991236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.093661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.197779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.305441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.402904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.508764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.620872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.735858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.860652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:53.963650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.074379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.191411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.302222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.539182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.648501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.762223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.870804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:54.980575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.099713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.218324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.333394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.438360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.541958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.659142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.775264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.897881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:55.999734image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.123488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.263033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.383214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.505765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.624619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.747795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.867826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:56.983014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.101071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.215694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.335749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.460479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.574791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.705552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.849140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:57.971333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.081633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.359304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.465377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.564343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.676092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.779384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.884747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:58.986674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.086872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.195934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.294745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.395084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.495455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.587063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.684279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.782861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:05:59.898614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.002602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.118276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.224293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.327403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.437587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.549982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.678134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.794166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:00.900169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.003416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.099410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.198788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.293482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.385225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.480741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.577732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.675628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.766970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.880620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:01.982949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.083574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.196436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.518407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.626989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.732356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.855798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:02.963076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.062635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.167517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.268316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.368225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.467988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.568019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.668253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.765695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:03.896756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.002795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.105646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.210661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.314599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.424675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.531817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.635076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.742338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.857956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:04.966947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.075176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.176905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.284360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.408239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.517071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.612207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.713808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.817527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:05.937110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.050507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.153936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.263297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.373557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.475699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.575972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.674349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.778505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.875181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:06.971493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.069904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.169619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.276561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.376336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.712995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.814795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:07.921459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.014539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.101833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.189617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.280075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.374562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.463662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.554549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.646089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.735725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.834540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:08.928679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.024272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.122113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.215378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.317237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.418349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.518718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.622727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.725712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.856205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:09.975430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.088182image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.220892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.333400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.443861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.548066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.650238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.764727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.878112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:10.986084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.083511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.179550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.286536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.392657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.497161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.592092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.694130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.804473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:11.925310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.026876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.120678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.218325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.318779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.411477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.508101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.620517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:12.879215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.013937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-24T20:06:13.212746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.317940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.432895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.526517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.625790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.720565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:13.819572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.274676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.369017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.456059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.538555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.621722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.712175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.807829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:14.923608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.012671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.112407image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.211914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.312711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.414836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.509269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.608434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.706518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:15.938139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.079764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.170663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.266829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.363493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.468015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.574703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.682408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.789900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.882524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:16.982382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.088064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.188726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.294158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.391663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.505247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.621053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.739334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.857921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:17.960170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.088799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.198750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.308645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.420041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.532754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.644386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.750384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.860357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:18.970764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.081357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.196295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.305549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.416508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.528156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.637868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.751230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.880602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:19.993150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.108838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.217046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.328846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.440279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.555133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.673787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.784808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.889793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:20.993464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.104270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.201630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.305072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.406703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.506813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.608972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.707507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.813655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:21.934731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:22.029883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:22.517570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:22.619353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-24T20:06:22.719880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-24T20:06:44.884002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-24T20:06:45.148046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-24T20:06:45.424889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-24T20:06:45.702323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-24T20:06:46.057400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-24T20:06:23.458114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexDT_GERACAOHH_GERACAOANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNA
0022/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE24171GAMELEIRA13VEREADOR17000106890011789ANDREIA LÚCIA DE FREITAS PEIXOTOANDREIA DE JUNIOR#NULO#45623252400ANDREIAFREITAS0405@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO11PPPROGRESSISTAS170000129245PARTIDO ISOLADOPP1BRASILEIRA NATAPE-3RIBEIRÃO04/05/196852121698708094FEMININO2LÊ E ESCREVE3CASADO(A)1BRANCA125ADMINISTRADOR12307.755SUPLENTENS-160028831202061700282DEFERIDO2DEFERIDOSIM
1122/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE23574CABO DE SANTO AGOSTINHO13VEREADOR17000067845212612MIZAEL JOSÉ DA SILVAMIZAEL DO POVO#NULO#5262182402FRENTEPOPULARCABO@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO12PDTPARTIDO DEMOCRÁTICO TRABALHISTA170000061270PARTIDO ISOLADOPDT1BRASILEIRA NATAPE-3CABO DE SANTO AGOSTINHO10/10/198040620088508412MASCULINO5ENSINO MÉDIO INCOMPLETO1SOLTEIRO(A)3PARDA999OUTROS109902.995SUPLENTENN-160034124202061701212DEFERIDO2DEFERIDOSIM
2222/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE26174TUPARETAMA13VEREADOR17000093057440400CARLOS ANTONIO NOGUEIRACARLINHOS DE CIBIU#NULO#11597546836PRISCILLAFILO2020@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO40PSBPARTIDO SOCIALISTA BRASILEIRO170000107226PARTIDO ISOLADOPSB1BRASILEIRA NATAPE-3SÃO JOSÉ DO EGITO01/12/196753197958408092MASCULINO4ENSINO FUNDAMENTAL COMPLETO3CASADO(A)3PARDA601AGRICULTOR12307.755SUPLENTENS-160012358202061700682DEFERIDO2DEFERIDOSIM
3322/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE24279GRAVATÁ13VEREADOR17000120071936222JOSÉ SOARES DA SILVAJUCA SOARES#NULO#3266048474PTCESTADUALPE@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO36PTCPARTIDO TRABALHISTA CRISTÃO170000147649PARTIDO ISOLADOPTC1BRASILEIRA NATAPE-3GRAVATÁ03/09/197743555314508842MASCULINO3ENSINO FUNDAMENTAL INCOMPLETO3CASADO(A)1BRANCA999OUTROS134612.464NÃO ELEITONN-160036012202061700302DEFERIDO2DEFERIDOSIM
4422/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE25798SERRITA13VEREADOR17000092027655999MARIA ZULEIDE DOMINGOSCÊ DE ASSIS VAQUEIRO#NULO#88289419449RCA109@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO55PSDPARTIDO SOCIAL DEMOCRÁTICO170000105823PARTIDO ISOLADOPSD1BRASILEIRA NATAPE-3SALGUEIRO26/12/197248407362008254FEMININO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA999OUTROS12307.755SUPLENTENS-160008607202061700762DEFERIDO2DEFERIDOSIM
5522/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE24279GRAVATÁ13VEREADOR17000108866343112AMARO JOÃO DOS SANTOSAMARO DO CARRO DE MÃO#NULO#86878735491CANDIDATOSPVGRAVATA@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO43PVPARTIDO VERDE170000131828PARTIDO ISOLADOPV1BRASILEIRA NATAPE-3GRAVATÁ21/03/197149360078408682MASCULINO2LÊ E ESCREVE1SOLTEIRO(A)3PARDA999OUTROS134612.465SUPLENTENN-160015580202061700302DEFERIDO2DEFERIDOSIM
6722/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE25259POMBOS13VEREADOR17000069036820620MARIA LIDIA DA SILVAIRMÃ MARIA#NULO#83486933434MVCONTABIL1969@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO20PSCPARTIDO SOCIAL CRISTÃO170000063749PARTIDO ISOLADOPSC1BRASILEIRA NATAPE-3POMBOS26/05/197050333328708844FEMININO4ENSINO FUNDAMENTAL COMPLETO3CASADO(A)1BRANCA176COZINHEIRO12307.755SUPLENTENS-160006805202061701022DEFERIDO2DEFERIDOSIM
7822/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE25119PAUDALHO13VEREADOR17000065868619222CRISTOVAM PEREIRA DA SILVA FILHOCRISTOVAM DA MISTA#NULO#58292896449CRISTOVAMPEREIRA51@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO19PODEPODEMOS170000058728PARTIDO ISOLADOPODE1BRASILEIRA NATAPE-3PAUDALHO10/05/196654390855508412MASCULINO8SUPERIOR COMPLETO3CASADO(A)3PARDA298SERVIDOR PÚBLICO MUNICIPAL17845.225SUPLENTENS-160004790202061700172DEFERIDO2DEFERIDOSIM
8922/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE23876CHÃ DE ALEGRIA13VEREADOR17000080437340555JOSE DIONISIO PEREIRADIONISIO DA VILA DO CAMPO#NULO#3073071407QSELEICOES2020@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO40PSBPARTIDO SOCIALISTA BRASILEIRO170000079484PARTIDO ISOLADOPSB1BRASILEIRA NATAPE-3NAZARÉ DA MATA26/09/198040511244608762MASCULINO5ENSINO MÉDIO INCOMPLETO3CASADO(A)1BRANCA169COMERCIANTE15585.595SUPLENTENS-160004426202061700212DEFERIDO2DEFERIDOSIM
91022/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALPE24112FLORES13VEREADOR17000065484240727AURICIA MARQUES NUNESLALA LIMA#NULO#68190620444MARCELAPLM@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO40PSBPARTIDO SOCIALISTA BRASILEIRO170000058366PARTIDO ISOLADOPSB1BRASILEIRA NATAPE-3SERRA TALHADA15/03/197050325409808174FEMININO8SUPERIOR COMPLETO3CASADO(A)1BRANCA124CONTADOR12307.755SUPLENTENS-160003098202061700672DEFERIDO2DEFERIDOSIM

Last rows

df_indexDT_GERACAOHH_GERACAOANO_ELEICAOCD_TIPO_ELEICAONM_TIPO_ELEICAONR_TURNOCD_ELEICAODS_ELEICAODT_ELEICAOTP_ABRANGENCIASG_UFSG_UENM_UECD_CARGODS_CARGOSQ_CANDIDATONR_CANDIDATONM_CANDIDATONM_URNA_CANDIDATONM_SOCIAL_CANDIDATONR_CPF_CANDIDATONM_EMAILCD_SITUACAO_CANDIDATURADS_SITUACAO_CANDIDATURACD_DETALHE_SITUACAO_CANDDS_DETALHE_SITUACAO_CANDTP_AGREMIACAONR_PARTIDOSG_PARTIDONM_PARTIDOSQ_COLIGACAONM_COLIGACAODS_COMPOSICAO_COLIGACAOCD_NACIONALIDADEDS_NACIONALIDADESG_UF_NASCIMENTOCD_MUNICIPIO_NASCIMENTONM_MUNICIPIO_NASCIMENTODT_NASCIMENTONR_IDADE_DATA_POSSENR_TITULO_ELEITORAL_CANDIDATOCD_GENERODS_GENEROCD_GRAU_INSTRUCAODS_GRAU_INSTRUCAOCD_ESTADO_CIVILDS_ESTADO_CIVILCD_COR_RACADS_COR_RACACD_OCUPACAODS_OCUPACAOVR_DESPESA_MAX_CAMPANHACD_SIT_TOT_TURNODS_SIT_TOT_TURNOST_REELEICAOST_DECLARAR_BENSNR_PROTOCOLO_CANDIDATURANR_PROCESSOCD_SITUACAO_CANDIDATO_PLEITODS_SITUACAO_CANDIDATO_PLEITOCD_SITUACAO_CANDIDATO_URNADS_SITUACAO_CANDIDATO_URNAST_CANDIDATO_INSERIDO_URNA
292593164622/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16330BOM JESUS13VEREADOR20000125204525555ROSIVALDO ABDIAS LINSPROFESSOR ROSIVALDO#NULO#61900494434ROSIVALDOABDIAS@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO25DEMDEMOCRATAS200000155448PARTIDO ISOLADODEM1BRASILEIRA NATARN-3BOM JESUS02/08/19675325313516602MASCULINO4ENSINO FUNDAMENTAL COMPLETO3CASADO(A)3PARDA298SERVIDOR PÚBLICO MUNICIPAL25323.675SUPLENTESS-160034282202062000052DEFERIDO2DEFERIDOSIM
292603164722/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN17876PAU DOS FERROS13VEREADOR20000114161540000JOÃO PAULO DA SILVAJOÃO PAULO RADIALISTA#NULO#6821893403JOCELITORRES12@HOTMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO40PSBPARTIDO SOCIALISTA BRASILEIRO200000138481PARTIDO ISOLADOPSB1BRASILEIRA NATAPB-3SOUSA19/08/198535232708816862MASCULINO3ENSINO FUNDAMENTAL INCOMPLETO3CASADO(A)3PARDA999OUTROS19165.904NÃO ELEITONS-160022753202062000402DEFERIDO2DEFERIDOSIM
292613164822/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16250CAMPO GRANDE13VEREADOR20000083076213000FRANCISCO BENEDITO DE PAULABENEDITO DE CAIANA#NULO#2794762486CARAMURUPAIVA13@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO13PTPARTIDO DOS TRABALHADORES200000087639PARTIDO ISOLADOPT1BRASILEIRA NATARN-3CAMPO GRANDE07/07/197644146942516512MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)1BRANCA601AGRICULTOR36879.975SUPLENTENS-160005711202062000312DEFERIDO2DEFERIDOSIM
292623164922/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16918IPANGUAÇU13VEREADOR20000091681577111FRANCIRLEIDE GONÇALVES DE SOUZACHYL SOUZA#NULO#8340098462CHYLSOUZA@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO77SOLIDARIEDADESOLIDARIEDADE200000105343PARTIDO ISOLADOSOLIDARIEDADE1BRASILEIRA NATARN-3ASSÚ19/08/199030270546416004FEMININO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA601AGRICULTOR20405.315SUPLENTENN-160016398202062000542DEFERIDO2DEFERIDOSIM
292633165022/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN17710PARANÁ13VEREADOR20000098588055523RAIMUNDO NONATO FERREIRA DA COSTAMOCÓ#NULO#29118535801IRANPINTO572@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO55PSDPARTIDO SOCIAL DEMOCRÁTICO200000115374PARTIDO ISOLADOPSD1BRASILEIRA NATARN-3PARANÁ22/10/197743180840816782MASCULINO4ENSINO FUNDAMENTAL COMPLETO1SOLTEIRO(A)3PARDA601AGRICULTOR12307.752ELEITO POR QPNS-160013909202062000422DEFERIDO2DEFERIDOSIM
292643165122/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16284PORTO DO MANGUE13VEREADOR20000079874022333DAMIAO BENTO DA SILVA FILHOJOFINHA#NULO#5482626407ABDSF4778@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO22PLPARTIDO LIBERAL200000078420PARTIDO ISOLADOPL1BRASILEIRA NATARN-3PENDÊNCIAS17/12/198040190431316942MASCULINO3ENSINO FUNDAMENTAL INCOMPLETO1SOLTEIRO(A)3PARDA397RECEPCIONISTA12307.755SUPLENTENN-160015238202062000322DEFERIDO2DEFERIDOSIM
292653165222/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN17272LAGOA DE VELHOS13VEREADOR20000099797110222MILENEMILENE#NULO#6963887403MILETETRAVASSOSDEARAUJO042@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO10REPUBLICANOSREPUBLICANOS200000117129PARTIDO ISOLADOREPUBLICANOS1BRASILEIRA NATARN-3SÃO PAULO DO POTENGI22/08/198733245568916004FEMININO6ENSINO MÉDIO COMPLETO3CASADO(A)1BRANCA601AGRICULTOR12307.755SUPLENTENS-160011295202062000192DEFERIDO2DEFERIDOSIM
292663165322/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN18538SÃO RAFAEL13VEREADOR20000064559411333MARIA SALETE FONSECASALETE#NULO#3154487417MARIASALETEFONSECA2020@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO11PPPROGRESSISTAS200000057175PARTIDO ISOLADOPP1BRASILEIRA NATARN-3SÃO RAFAEL31/12/197446148284616434FEMININO3ENSINO FUNDAMENTAL INCOMPLETO3CASADO(A)1BRANCA999OUTROS21492.475SUPLENTENS-160007022202062000272DEFERIDO2DEFERIDOSIM
292673165422/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16039ASSÚ13VEREADOR20000085535117123FRANCISCO RIVONALDO DE SOUZARIVONALDO GARÇOM#NULO#850229421FLAVIOCONSULTORIAADM@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO17PSLPARTIDO SOCIAL LIBERAL200000092778PARTIDO ISOLADOPSL1BRASILEIRA NATARN-3ASSÚ11/10/198040190358316782MASCULINO6ENSINO MÉDIO COMPLETO1SOLTEIRO(A)3PARDA713CARPINTEIRO, MARCENEIRO E ASSEMELHADOS41328.044NÃO ELEITONS-160011180202062000292DEFERIDO2DEFERIDOSIM
292683165522/07/202119:18:4920202ELEIÇÃO ORDINÁRIA1426Eleições Municipais 202015/11/2020MUNICIPALRN16438CANGUARETAMA13VEREADOR20000107105751345GEOVANY JUVENAL DUARTEGEOVANY#NULO#3423734477DUARTEDUARTE201820192020@GMAIL.COM12APTO2DEFERIDOPARTIDO ISOLADO51PATRIOTAPATRIOTA200000129512PARTIDO ISOLADOPATRIOTA1BRASILEIRA NATARN-3CANGUARETAMA22/10/197941173149416192MASCULINO5ENSINO MÉDIO INCOMPLETO1SOLTEIRO(A)3PARDA169COMERCIANTE24352.564NÃO ELEITONS-160046562202062000112DEFERIDO2DEFERIDOSIM